{"version":"1.0","generated":"2026-06-20T00:10:24.130Z","description":"Unified QIF dataset: threat techniques, BCI devices, brain atlas, physics constraints, and scoring systems. All cross-referenced by QIF hourglass band IDs.","hourglass_bands":[{"id":"N7","name":"Neocortex","zone":"neural","color":"#166534","description":"PFC, M1, V1, Broca, Wernicke — executive function, language, movement, perception"},{"id":"N6","name":"Limbic System","zone":"neural","color":"#3a7d44","description":"Hippocampus, amygdala, insula — emotion, memory, interoception"},{"id":"N5","name":"Basal Ganglia","zone":"neural","color":"#5c7a38","description":"Striatum, STN, substantia nigra — motor selection, reward, habit"},{"id":"N4","name":"Diencephalon","zone":"neural","color":"#72772f","description":"Thalamus, hypothalamus — sensory gating, consciousness relay"},{"id":"N3","name":"Cerebellum","zone":"neural","color":"#877226","description":"Cerebellar cortex, deep nuclei — motor coordination, timing"},{"id":"N2","name":"Brainstem","zone":"neural","color":"#9b6c1e","description":"Medulla, pons, midbrain — vital functions, arousal, reflexes"},{"id":"N1","name":"Spinal Cord","zone":"neural","color":"#ae6616","description":"Cervical through sacral — reflexes, peripheral relay"},{"id":"I0","name":"Neural Interface","zone":"interface","color":"#f59e0b","description":"Electrode-tissue boundary — measurement/collapse, quasi-quantum zone"},{"id":"S1","name":"Near-Field / On-Device","zone":"synthetic","color":"#93c5fd","description":"Amplification, ADC, near-field EM coupling (0-10 kHz, on-device)"},{"id":"S2","name":"Guided-Wave / Host-Local","zone":"synthetic","color":"#60a5fa","description":"Firmware, drivers, host compute, USB, decoding, BLE/WiFi baseband (10 kHz - 1 GHz, device-local)"},{"id":"S3","name":"Far-Field / Wide-Area","zone":"synthetic","color":"#3b82f6","description":"RF transmission, directed energy, application layer (1 GHz+, off-device)"}],"threats":{"techniques":[{"id":"QIF-T0001","name":"Signal injection","nameClinical":"tDCS/tACS neuromodulation","category":"SI","tactic":"QIF-N.IJ","bands":["I0","N1"],"severity":"high","status":"CONFIRMED","coupling":null,"access":null,"classicalDetection":"Yes","quantumDetection":"Enhanced (coherence metric)","description":"Inject crafted signals mimicking legitimate brain activity at electrode-tissue boundary. Classical detection via impedance anomaly. QI coherence metric flags phase/timing inconsistency.","bandsStr":"I0–N1","niss":{"version":"1.1","vector":"NISS:1.1/BI:H/CR:H/CD:H/CV:E/RV:P/NP:T","score":6.1,"severity":"medium","pins":true},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:P/AC:L/AT:P/PR:L/UI:N/VC:L/VI:H/VA:H/SC:N/SI:H/SA:L","supplemental":"S:P/AU:N/R:U/V:C","gap_group":3,"gap_summary":"Tissue harm from signal injection and cognitive alteration not expressible in CVSS"},"crossRefs":{"related_ids":["T1659","T1674"]},"sources":["Kohno et al. 2009","Bonaci et al. 2015"],"tara":{"mechanism":"Electrical current delivery at electrode-tissue interface modulating local field potentials","dual_use":"confirmed","clinical":{"therapeutic_analog":"tDCS/tACS neuromodulation","conditions":["major depressive disorder","chronic pain","stroke rehabilitation","tinnitus"],"fda_status":"cleared","evidence_level":"RCT","safe_parameters":"1-2 mA, 20-30 min sessions, 35 cm² electrode area","sources":["Brunoni et al. 2012 (Arch Gen Psychiatry)","Lefaucheur et al. 2017 (Clin Neurophysiol)"]},"governance":{"consent_tier":"enhanced","monitoring":["impedance","stimulation_waveform","tissue_temperature","patient_response"],"regulations":["FDA 510(k)/PMA","IEC 60601-1","ISO 80601-2-10","21 CFR 882"],"data_classification":"PHI","safety_ceiling":"2 mA current, 30 min/session, 7 sessions/week max"},"engineering":{"coupling":["electromagnetic"],"parameters":{"frequency_hz":"DC (tDCS) or 0.1-100 (tACS)","amplitude_mA":"0.5-2.0","duration_s":"600-1800"},"hardware":["stimulation_electrodes","constant_current_source","impedance_monitor"],"detection":"Impedance anomaly detection, waveform verification, current leakage monitoring"},"dsm5":{"primary":[{"code":"F43.2","name":"Adjustment Disorder","confidence":"established"},{"code":"F45","name":"Somatoform disorders","confidence":"established"},{"code":"F44.4","name":"Conversion Disorder","confidence":"established"}],"secondary":[{"code":"F82","name":"Developmental Coordination Disorder","confidence":"probable"}],"risk_class":"direct","cluster":"motor_neurocognitive","pathway":"I0 (electrode-tissue boundary) → measurement; N1 (spinal cord) → reflexes","niss_correlation":"BI:H,CR:H,CD:H,CV:E,RV:P → motor/neurocognitive cluster"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Noninvasive stimulation (tDCS/tACS) exists today","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","DI","PC"],"cci":0.96},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":false,"network_connectable":false,"vulnerable":true},"applicable_requirements":["TM","VA","SA"],"coverage_score":0.7,"gaps":["CVSS cannot express neural-specific impacts","Consent complexity under-matches neural impact (CCI/NISS mismatch)"]}},"taraAlias":"TARA-SOM-M-001","taraDomainPrimary":"SOM","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0002","name":"Neural ransomware","nameClinical":"Deep brain stimulation (DBS) / Responsive neurostimulation (RNS)","category":"DS","tactic":"QIF-P.DS","bands":["N3","N7","N6"],"severity":"critical","status":"EMERGING","coupling":null,"access":null,"classicalDetection":"Partial","quantumDetection":"Yes (QI score drop)","description":"Disrupt or lock neural function via stimulation manipulation. Closed-loop devices (RNS, DBS) most vulnerable. QI detects anomalous coherence collapse. Distinct from Neural DoS: ransomware implies conditional restoration.","bandsStr":"N3–N7","niss":{"version":"1.1","vector":"NISS:1.1/BI:H/CR:H/CD:H/CV:E/RV:P/NP:T","score":6.1,"severity":"medium","pins":true},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:H/AT:P/PR:L/UI:N/VC:L/VI:H/VA:H/SC:N/SI:H/SA:H","supplemental":"S:P/AU:N/R:U/V:C","gap_group":3,"gap_summary":"Neural function locking and conditional restoration have no CVSS equivalent"},"crossRefs":{"related_ids":["T1486","T1489"]},"sources":["Pycroft et al. 2016"],"tara":{"mechanism":"Disruption or conditional locking of neural function via closed-loop stimulation parameter manipulation","dual_use":"confirmed","clinical":{"therapeutic_analog":"Deep brain stimulation (DBS) / Responsive neurostimulation (RNS)","conditions":["Parkinson's disease","essential tremor","epilepsy","treatment-resistant depression"],"fda_status":"approved","evidence_level":"RCT","safe_parameters":"Device-specific (Medtronic, NeuroPace): 1-5V, 60-450μs pulse width, 130-185 Hz","sources":["Lozano et al. 2019 (Nature Reviews Neuroscience)","Morrell 2011 (Neurosurgery)"]},"governance":{"consent_tier":"IRB","monitoring":["impedance","stimulation_waveform","tissue_temperature","patient_response"],"regulations":["FDA 510(k)/PMA","IEC 60601-1","ISO 80601-2-10","21 CFR 882"],"data_classification":"PHI","safety_ceiling":"Device-specific FDA-approved parameters, fail-safe shutoff mandatory"},"engineering":{"coupling":["electromagnetic"],"parameters":{"frequency_hz":"130-185","amplitude_V":"1-5","pulse_width_us":"60-450"},"hardware":["implanted_electrodes","pulse_generator","sensing_amplifier","telemetry_module"],"detection":"Stimulation parameter monitoring, impedance trending, battery state tracking"},"dsm5":{"primary":[{"code":"F82","name":"Developmental Coordination Disorder","confidence":"established"},{"code":"F84","name":"Pervasive developmental disorders","confidence":"established"},{"code":"F20","name":"Schizophrenia Spectrum","confidence":"established"},{"code":"F32","name":"Major Depressive Disorder","confidence":"established"},{"code":"F90","name":"ADHD","confidence":"established"},{"code":"F42","name":"OCD","confidence":"established"},{"code":"F41.1","name":"Generalized Anxiety Disorder","confidence":"established"},{"code":"F43.10","name":"PTSD","confidence":"established"},{"code":"F44","name":"Dissociative Disorders","confidence":"established"}],"secondary":[{"code":"F30","name":"Manic episode","confidence":"probable"},{"code":"F01","name":"Vascular dementia","confidence":"established"},{"code":"F43","name":"PTSD / Trauma","confidence":"established"},{"code":"F80","name":"Communication Disorders","confidence":"established"},{"code":"F60","name":"Personality Disorders","confidence":"probable"},{"code":"F63","name":"Impulse-Control Disorders","confidence":"probable"},{"code":"F98.4","name":"Stereotyped movement disorders","confidence":"established"},{"code":"F50","name":"Eating Disorders","confidence":"probable"},{"code":"F10","name":"Alcohol-related disorders (F10)","confidence":"established"},{"code":"F45","name":"Somatoform disorders","confidence":"probable"}],"risk_class":"direct","cluster":"cognitive_psychotic","pathway":"N7 (PFC/M1) → executive function; N6 (hippocampus/amygdala) → emotion regulation","niss_correlation":"BI:H,CR:H,CD:H,CV:E,RV:P → cognitive/psychotic cluster"}},"physicsFeasibility":{"tier":2,"tier_label":"mid_term","timeline":"2031-2038","gate_reason":"Selective neural ransomware needs bidirectional 10k+ channel implant; thermal budget gates channel count at current process nodes. Note: Selective neural ransomware requires 10k+ channels (Tier 2). Coarse DBS/RNS parameter ransomware is Tier 0 with existing device access — see technique description for scope.","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","DI","PC"],"cci":1.8},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.6,"gaps":["CVSS cannot express neural-specific impacts"]}},"taraAlias":"TARA-MOT-D-001","taraDomainPrimary":"MOT","taraDomainSecondary":[],"taraMode":"D"},{"id":"QIF-T0003","name":"Eavesdropping / signal interception","nameClinical":"EEG/ECoG diagnostic monitoring","category":"SE","tactic":"QIF-D.HV","bands":["I0","N1","S1","S2","S3"],"severity":"high","status":"CONFIRMED","coupling":null,"access":null,"classicalDetection":"Partial","quantumDetection":"Yes (Heisenberg disturbance at I0)","description":"Passive interception of neural signals. At I0: quantum measurement disturbs state (detectable). At S1-S3: classical RF interception, most consumer BCIs transmit unencrypted.","bandsStr":"I0–S3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:L/CD:L/CV:I/RV:F/NP:N","score":2.7,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N","supplemental":"S:N/AU:Y/R:A/V:C","gap_group":3,"gap_summary":"Passive neural thought interception — mental privacy not covered by CVSS confidentiality"},"crossRefs":{"related_ids":["T1040"]},"sources":["Kohno et al. 2009","Landau et al. 2020","Schroder et al. 2025"],"tara":{"mechanism":"Passive capture of neural electromagnetic emissions from BCI data pathways","dual_use":"confirmed","clinical":{"therapeutic_analog":"EEG/ECoG diagnostic monitoring","conditions":["epilepsy diagnosis","sleep disorders","cognitive assessment","intraoperative monitoring"],"fda_status":"cleared","evidence_level":"meta_analysis","safe_parameters":"Non-invasive; passive recording only; no stimulation","sources":["Niedermeyer & da Silva 2004 (Electroencephalography)","Schalk & Leuthardt 2011 (IEEE)"]},"governance":{"consent_tier":"enhanced","monitoring":["signal_quality","data_encryption_status","access_audit_log"],"regulations":["HIPAA","GDPR Art. 9","21 CFR Part 11","IEC 62304"],"data_classification":"sensitive_neural","safety_ceiling":"Passive recording; data retention and access controls are primary safety concern"},"engineering":{"coupling":["electromagnetic"],"parameters":{"frequency_hz":"0.1-1000 (broadband capture)","sensitivity_uV":"0.1-100"},"hardware":["recording_electrodes","amplifier","ADC","wireless_transmitter"],"detection":"RF spectrum monitoring, cable shielding verification, encryption validation"},"dsm5":{"primary":[{"code":"F43.2","name":"Adjustment Disorder","confidence":"established"},{"code":"F45","name":"Somatoform disorders","confidence":"established"},{"code":"F44.4","name":"Conversion Disorder","confidence":"established"}],"secondary":[{"code":"F82","name":"Developmental Coordination Disorder","confidence":"probable"}],"risk_class":"direct","cluster":"motor_neurocognitive","pathway":"I0 (electrode-tissue boundary) → measurement; N1 (spinal cord) → reflexes","niss_correlation":"CV:I → motor/neurocognitive cluster"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"EEG recording hardware widely available","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","MI","DI"],"cci":0.72},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.4,"gaps":["CVSS cannot express neural-specific impacts","No FDA pathway for consumer sensor exploitation"]}},"taraAlias":"TARA-COG-R-001","taraDomainPrimary":"COG","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0004","name":"Man-in-the-middle","nameClinical":"Signal routing in closed-loop neuroprosthetics","category":"DM","tactic":"QIF-D.HV","bands":["I0","S1","S2"],"severity":"critical","status":"DEMONSTRATED","coupling":null,"access":null,"classicalDetection":"Partial","quantumDetection":"Yes (no-cloning + Bell test)","description":"Intercept and modify signals at I0 boundary or BCI telemetry. No-cloning theorem prevents perfect copy of quantum neural states. Bell test detects entanglement disruption.","bandsStr":"I0–S2","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:L/CD:L/CV:I/RV:F/NP:N","score":2.7,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:L/AT:P/PR:L/UI:N/VC:H/VI:H/VA:L/SC:H/SI:H/SA:L","supplemental":"S:P/AU:N/R:U/V:C","gap_group":3,"gap_summary":"Neural signal modification in transit affects cognition beyond data integrity"},"crossRefs":{"related_ids":["T1557"],"secondary_tactics":["QIF-N.IJ"]},"sources":["Martinovic et al. 2012"],"tara":{"mechanism":"Active interception and modification of signals between BCI components in transit","dual_use":"probable","clinical":{"therapeutic_analog":"Signal routing in closed-loop neuroprosthetics","conditions":["spinal cord injury (signal bridging)","paralysis (motor signal rerouting)"],"fda_status":"investigational","evidence_level":"preclinical","safe_parameters":"Signal fidelity >99.9%, latency <10ms, bidirectional verification","sources":["Bensmaia & Miller 2014 (Science)","Ethier et al. 2012 (Nature)"]},"governance":{"consent_tier":"IRB","monitoring":["impedance","stimulation_waveform","tissue_temperature","patient_response"],"regulations":["FDA 510(k)/PMA","IEC 60601-1","ISO 80601-2-10","21 CFR 882"],"data_classification":"PHI","safety_ceiling":"Signal integrity verification mandatory; fail-open to safe state"},"engineering":{"coupling":["electromagnetic"],"parameters":{"latency_ms":"<10","bandwidth_kbps":"variable","encryption":"required"},"hardware":["signal_interceptor","protocol_analyzer","real_time_processor"],"detection":"End-to-end latency monitoring, cryptographic integrity checks, signal fingerprinting"},"dsm5":{"primary":[{"code":"F43.2","name":"Adjustment Disorder","confidence":"established"}],"secondary":[],"risk_class":"direct","cluster":"mood_trauma","pathway":"I0 (electrode-tissue boundary) → measurement","niss_correlation":"CV:I → mood/trauma cluster"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Signal interception at I0 with existing equipment","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["CL","MI","PC","DI"],"cci":1.8},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.7,"gaps":["CVSS cannot express neural-specific impacts"]}},"taraAlias":"TARA-SIL-R-001","taraDomainPrimary":"COG","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0005","name":"Quantum tunneling exploit","nameClinical":"Quantum sensing for neural diagnostics (NV-center magnetometry)","category":"SI","tactic":"QIF-N.IJ","bands":["I0","N1"],"severity":"high","status":"THEORETICAL","coupling":null,"access":null,"classicalDetection":"No","quantumDetection":"Yes (tunneling profile anomaly)","description":"Exploit ion channel quantum tunneling to inject false synaptic events. Detectable via Q_tunnel term anomaly in QI equation. Requires understanding of target's ion channel tunneling profile.","bandsStr":"I0–N1","niss":{"version":"1.1","vector":"NISS:1.1/BI:H/CR:H/CD:H/CV:E/RV:P/NP:T","score":6.1,"severity":"medium","pins":true},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:P/AC:H/AT:P/PR:L/UI:N/VC:L/VI:H/VA:L/SC:N/SI:H/SA:N","supplemental":"S:P/AU:N/R:U/V:C","gap_group":3,"gap_summary":"Ion-channel quantum manipulation has no CVSS physical impact analog"},"crossRefs":null,"sources":["Summhammer et al. 2012","Salari et al. 2015"],"tara":{"mechanism":"Exploitation of quantum tunneling effects at nanoscale electrode-tissue junctions","dual_use":"possible","clinical":{"therapeutic_analog":"Quantum sensing for neural diagnostics (NV-center magnetometry)","conditions":["high-resolution neural imaging","single-neuron recording"],"fda_status":"none","evidence_level":"preclinical","safe_parameters":"Passive quantum sensing; no stimulation; sub-nT field measurement","sources":["Barry et al. 2016 (PNAS, NV-center magnetometry)"]},"governance":{"consent_tier":"IRB","monitoring":["signal_quality","data_encryption_status","access_audit_log"],"regulations":["HIPAA","GDPR Art. 9","21 CFR Part 11","IEC 62304"],"data_classification":"sensitive_neural","safety_ceiling":"Quantum sensing is passive; data sensitivity is primary concern"},"engineering":{"coupling":["electromagnetic"],"parameters":{"scale_nm":"1-100","temperature_K":"physiological (310)"},"hardware":["nanoscale_electrodes","quantum_sensor","cryogenic_or_RT_readout"],"detection":"Tunneling current anomaly detection, junction impedance spectroscopy"},"dsm5":{"primary":[{"code":"F43.2","name":"Adjustment Disorder","confidence":"established"},{"code":"F45","name":"Somatoform disorders","confidence":"established"},{"code":"F44.4","name":"Conversion Disorder","confidence":"established"}],"secondary":[{"code":"F82","name":"Developmental Coordination Disorder","confidence":"probable"}],"risk_class":"indirect","cluster":"motor_neurocognitive","pathway":"I0 (electrode-tissue boundary) → measurement; N1 (spinal cord) → reflexes","niss_correlation":"BI:H,CR:H,CD:H,CV:E,RV:P → motor/neurocognitive cluster"}},"physicsFeasibility":{"tier":3,"tier_label":"far_term","timeline":"2038+","gate_reason":"Quantum tunneling exploit needs ~10nm electrodes (3 orders of magnitude below current ~10um); quantum-regime BCI","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","DI","PC"],"cci":1.44},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":false,"network_connectable":false,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.2,"gaps":["CVSS cannot express neural-specific impacts","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-SOM-M-002","taraDomainPrimary":"SOM","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0006","name":"Davydov soliton attack","nameClinical":"Biophoton/soliton-based cellular signaling research","category":"SI","tactic":"QIF-N.IJ","bands":["I0","N1"],"severity":"high","status":"THEORETICAL","coupling":null,"access":null,"classicalDetection":"No","quantumDetection":"Yes (tunneling term Q_tunnel)","description":"THz stimulation triggers Davydov solitons in SNARE protein complexes, causing false neurotransmitter release at I0. Exploits energy transport in alpha-helix protein structures.","bandsStr":"I0–N1","niss":{"version":"1.1","vector":"NISS:1.1/BI:H/CR:H/CD:H/CV:E/RV:P/NP:T","score":6.1,"severity":"medium","pins":true},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:P/AC:H/AT:P/PR:L/UI:N/VC:L/VI:H/VA:L/SC:N/SI:L/SA:N","supplemental":"S:P/AU:N/R:U/V:C","gap_group":3,"gap_summary":"Protein-level soliton manipulation is below CVSS's abstraction layer"},"crossRefs":null,"sources":["Davydov 1973","Scott 1992"],"tara":{"mechanism":"Davydov soliton propagation in protein alpha-helices at electrode-tissue interface","dual_use":"possible","clinical":{"therapeutic_analog":"Biophoton/soliton-based cellular signaling research","conditions":["theoretical: targeted molecular signaling"],"fda_status":"none","evidence_level":"theoretical","safe_parameters":"No established parameters; theoretical mechanism","sources":["Davydov 1973 (J Theor Biol)","Scott 1992 (Phys Rep)"]},"governance":{"consent_tier":"IRB","monitoring":["signal_quality","data_encryption_status","access_audit_log"],"regulations":["HIPAA","GDPR Art. 9","21 CFR Part 11","IEC 62304"],"data_classification":"sensitive_neural","safety_ceiling":"Theoretical; no safe parameters established"},"engineering":{"coupling":["mechanical","thermal"],"parameters":{"propagation_velocity_m_s":"~1000","energy_meV":"~20"},"hardware":["molecular_scale_probes","infrared_spectroscopy"],"detection":"Infrared absorption spectroscopy, protein conformational monitoring"},"dsm5":{"primary":[{"code":"F43.2","name":"Adjustment Disorder","confidence":"established"},{"code":"F45","name":"Somatoform disorders","confidence":"established"},{"code":"F44.4","name":"Conversion Disorder","confidence":"established"}],"secondary":[{"code":"F82","name":"Developmental Coordination Disorder","confidence":"probable"}],"risk_class":"indirect","cluster":"motor_neurocognitive","pathway":"I0 (electrode-tissue boundary) → measurement; N1 (spinal cord) → reflexes","niss_correlation":"BI:H,CR:H,CD:H,CV:E,RV:P → motor/neurocognitive cluster"}},"physicsFeasibility":{"tier":3,"tier_label":"far_term","timeline":"2038+","gate_reason":"Davydov soliton attack needs molecular-scale probes for protein lattice interaction","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","DI","PC"],"cci":1.44},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":false,"network_connectable":false,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.2,"gaps":["CVSS cannot express neural-specific impacts","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-SOM-M-003","taraDomainPrimary":"SOM","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0007","name":"Protocol manipulation","nameClinical":"Adaptive stimulation protocol adjustment in closed-loop BCIs","category":"DM","tactic":"QIF-N.IJ","bands":["S1","S2"],"severity":"medium","status":"DEMONSTRATED","coupling":null,"access":null,"classicalDetection":"Yes","quantumDetection":"Enhanced (protocol integrity via QI)","description":"Exploit weaknesses in BCI data protocols to inject commands or alter data formatting, bypassing security controls. Targets the digital decoding/telemetry pipeline.","bandsStr":"S1–S2","niss":{"version":"1.1","vector":"NISS:1.1/BI:H/CR:H/CD:H/CV:N/RV:P/NP:N","score":4.1,"severity":"medium","pins":true},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:H/AT:P/PR:L/UI:N/VC:L/VI:H/VA:L/SC:N/SI:H/SA:L","supplemental":"S:P/AU:N/R:U/V:D","gap_group":2,"gap_summary":"Classical pipeline corruption with downstream neural impact partially captured by Safety"},"crossRefs":{"related_ids":["T1565","T1565.002"]},"sources":["Schroder et al. 2025"],"tara":{"mechanism":"Manipulation of BCI communication protocol handshakes, headers, or sequencing","dual_use":"probable","clinical":{"therapeutic_analog":"Adaptive stimulation protocol adjustment in closed-loop BCIs","conditions":["epilepsy (responsive stimulation)","Parkinson's (adaptive DBS)"],"fda_status":"approved","evidence_level":"RCT","safe_parameters":"Protocol changes within FDA-cleared parameter envelope only","sources":["Little et al. 2013 (Ann Neurol, adaptive DBS)"]},"governance":{"consent_tier":"standard","monitoring":["firmware_integrity","access_logging","network_traffic"],"regulations":["FDA 21 CFR 820","IEC 62443","NIST CSF"],"data_classification":"restricted","safety_ceiling":"Protocol modifications logged and bounded by device safety envelope"},"engineering":{"coupling":["electromagnetic"],"parameters":{"protocol_layer":"application/transport","latency_impact_ms":"variable"},"hardware":["protocol_analyzer","BCI_firmware_interface"],"detection":"Protocol conformance testing, sequence number validation, timing analysis"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"BCI protocol analysis with existing tools","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI"],"cci":0.3},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.8,"gaps":["CVSS partially captures risk; neural dimensions missing"]}},"taraAlias":"TARA-SIL-M-001","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0008","name":"Command hijacking","nameClinical":"BCI command interfaces for motor-impaired patients","category":"SI","tactic":"QIF-N.IJ","bands":["S2","N7","N6","N5"],"severity":"high","status":"THEORETICAL","coupling":null,"access":null,"classicalDetection":"Partial","quantumDetection":"Enhanced (intent verification via QI)","description":"Intercept and modify motor commands or cognitive instructions in transit through the BCI system. Targets closed-loop stimulation devices. 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High-density arrays (Neuralink, Utah) approach word-level accuracy. Consumer EEG at phoneme level. Merges with 'Covert Speech Decoding' from 2024-2026 research.","bandsStr":"N7","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:C/CD:C/CV:E/RV:F/NP:N","score":3.4,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:H/AT:P/PR:L/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N","supplemental":"S:P/AU:N/R:U/V:C","gap_group":3,"gap_summary":"Inner speech decoding violates cognitive sovereignty — no CVSS equivalent"},"crossRefs":{"related_ids":["T1119","T1005"],"secondary_tactics":["QIF-D.HV"]},"sources":["Willett et al. 2023 (Nature)","Meta FAIR 2023"],"tara":{"mechanism":"Decoding internal speech or intended communication from neural activity patterns","dual_use":"confirmed","clinical":{"therapeutic_analog":"Speech neuroprosthetics (neural speech decoding)","conditions":["aphasia","ALS","locked-in syndrome","laryngectomy"],"fda_status":"breakthrough","evidence_level":"cohort","safe_parameters":"Patient-initiated decoding only; opt-in per session; data encryption at source","sources":["Moses et al. 2021 (NEJM, UCSF)","Willett et al. 2023 (Nature, Stanford)"]},"governance":{"consent_tier":"enhanced","monitoring":["cognitive_assessment","behavioral_tracking","informed_consent_renewal"],"regulations":["HIPAA","GDPR Art. 9","Common Rule (45 CFR 46)","proposed neurorights legislation"],"data_classification":"sensitive_neural","safety_ceiling":"Decoding only when explicitly activated; no passive monitoring; thought privacy protections"},"engineering":{"coupling":["electromagnetic"],"parameters":{"resolution":"phoneme or word level","accuracy":"50-95% (varies by system)","latency_ms":"<1000"},"hardware":["high_density_ECoG_or_Utah_array","neural_decoder","language_model"],"detection":"Decode activation monitoring, unauthorized access detection, data provenance tracking"},"dsm5":{"primary":[{"code":"F20","name":"Schizophrenia Spectrum","confidence":"established"},{"code":"F32","name":"Major Depressive Disorder","confidence":"established"},{"code":"F90","name":"ADHD","confidence":"established"},{"code":"F42","name":"OCD","confidence":"established"}],"secondary":[{"code":"F30","name":"Manic episode","confidence":"established"},{"code":"F43","name":"PTSD / Trauma","confidence":"established"},{"code":"F80","name":"Communication Disorders","confidence":"established"},{"code":"F60","name":"Personality Disorders","confidence":"probable"},{"code":"F63","name":"Impulse-Control Disorders","confidence":"probable"},{"code":"F01","name":"Vascular dementia","confidence":"established"},{"code":"F98.4","name":"Stereotyped movement disorders","confidence":"established"}],"risk_class":"direct","cluster":"cognitive_psychotic","pathway":"N7 (PFC/M1) → executive function","niss_correlation":"CR:C,CD:C,CV:E → cognitive/psychotic cluster"}},"physicsFeasibility":{"tier":1,"tier_label":"near_term","timeline":"2026-2031","gate_reason":"Covert thought decoding needs higher electrode density than current ECoG","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","DI"],"cci":1.2},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","PM"],"coverage_score":0.3,"gaps":["CVSS cannot express neural-specific impacts"]}},"taraAlias":"TARA-LNG-R-001","taraDomainPrimary":"COG","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0037","name":"Agency manipulation","nameClinical":"Neurofeedback for self-regulation / agency restoration post-stroke","category":"CI","tactic":"QIF-C.EX","bands":["N5","N6","N7"],"severity":"critical","status":"THEORETICAL","coupling":null,"access":null,"classicalDetection":"No","quantumDetection":"Enhanced (agency coherence via QI)","description":"Manipulate sense of agency -- user believes externally triggered actions are self-initiated, or vice versa. Targets basal ganglia (motor selection) and PFC (executive control). Neurorights: cognitive liberty.","bandsStr":"N5–N7","niss":{"version":"1.1","vector":"NISS:1.1/BI:L/CR:C/CD:C/CV:E/RV:P/NP:S","score":7.4,"severity":"high","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:H/AT:P/PR:L/UI:N/VC:L/VI:H/VA:L/SC:N/SI:H/SA:L","supplemental":"S:P/AU:N/R:U/V:C","gap_group":3,"gap_summary":"Free will violation and agency manipulation have no CVSS mapping"},"crossRefs":{"secondary_tactics":["QIF-P.DS"]},"sources":["Yuste et al. 2017 (Neurorights)","Goering et al. 2021"],"tara":{"mechanism":"Manipulation of sense of agency (ownership of actions/thoughts) via BCI-mediated stimulation or feedback","dual_use":"confirmed","clinical":{"therapeutic_analog":"Neurofeedback for self-regulation / agency restoration post-stroke","conditions":["stroke rehabilitation (motor agency)","schizophrenia (agency disturbance)","dissociative disorders"],"fda_status":"investigational","evidence_level":"cohort","safe_parameters":"Patient retains veto power; agency assessment scales administered regularly","sources":["Haggard 2017 (Nat Rev Neurosci)","Braun et al. 2018 (Cortex)"]},"governance":{"consent_tier":"IRB","monitoring":["cognitive_assessment","behavioral_tracking","informed_consent_renewal"],"regulations":["HIPAA","GDPR Art. 9","Common Rule (45 CFR 46)","proposed neurorights legislation"],"data_classification":"sensitive_neural","safety_ceiling":"Mandatory agency assessment; patient veto always available; ethics board review for any agency-affecting protocol"},"engineering":{"coupling":["electromagnetic"],"parameters":{"agency_metric":"intentional_binding_ms","feedback_modality":"visual/haptic/neural"},"hardware":["BCI_system","agency_measurement_tools","feedback_display"],"detection":"Agency scale monitoring, intentional binding measurement, self-report tracking"},"dsm5":{"primary":[{"code":"F90","name":"ADHD","confidence":"established"},{"code":"F10","name":"Alcohol-related disorders (F10)","confidence":"established"},{"code":"F42","name":"OCD","confidence":"established"},{"code":"F95","name":"Tic Disorders","confidence":"established"},{"code":"F32","name":"Major Depressive Disorder","confidence":"established"},{"code":"F41.1","name":"Generalized Anxiety Disorder","confidence":"established"},{"code":"F43.10","name":"PTSD","confidence":"established"},{"code":"F44","name":"Dissociative Disorders","confidence":"established"},{"code":"F20","name":"Schizophrenia Spectrum","confidence":"established"}],"secondary":[{"code":"F50","name":"Eating Disorders","confidence":"probable"},{"code":"F30","name":"Manic episode","confidence":"established"},{"code":"F60","name":"Personality Disorders","confidence":"established"},{"code":"F45","name":"Somatoform disorders","confidence":"probable"},{"code":"F63","name":"Impulse-Control Disorders","confidence":"probable"},{"code":"F01","name":"Vascular dementia","confidence":"established"},{"code":"F43","name":"PTSD / Trauma","confidence":"established"},{"code":"F80","name":"Communication Disorders","confidence":"established"},{"code":"F98.4","name":"Stereotyped movement disorders","confidence":"established"}],"risk_class":"direct","cluster":"cognitive_psychotic","pathway":"N7 (PFC/M1) → executive function; N6 (hippocampus/amygdala) → emotion regulation","niss_correlation":"CR:C,CD:C,CV:E,RV:P,NP:S → cognitive/psychotic cluster"},"icd10":{"secondary":[{"code":"G25.89","name":"Other specified extrapyramidal and movement disorders","confidence":"established"}],"risk_class":"direct","cluster":"cognitive_psychotic"}},"physicsFeasibility":{"tier":2,"tier_label":"mid_term","timeline":"2031-2038","gate_reason":"Agency manipulation needs simultaneous read of intention + write to motor/decision circuits","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","DI","PC"],"cci":1.8},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.2,"gaps":["CVSS cannot express neural-specific impacts","High neural impact (NISS >= 7.0) without IEC 62443 coverage","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-IDN-M-003","taraDomainPrimary":"COG","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0038","name":"Brainprint theft","nameClinical":"Brain fingerprinting / neural identity verification","category":"EX","tactic":"QIF-C.EX","bands":["N6","N7"],"severity":"high","status":"EMERGING","coupling":null,"access":null,"classicalDetection":"Yes","quantumDetection":"Enhanced (quantum biometric if proven)","description":"Extract the user's unique brainprint (ERP template, spectral fingerprint) for later replay/spoofing. Unlike passwords, neural biometrics cannot be changed. 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Targets insula, TPJ, mPFC. Could cause depersonalization, dissociation, or false self-recognition. 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Tamper-evident seals for implants.","bandsStr":"I0","niss":{"version":"1.1","vector":"NISS:1.1/BI:H/CR:H/CD:H/CV:E/RV:P/NP:T","score":6.1,"severity":"medium","pins":true},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:P/AC:L/AT:P/PR:N/UI:N/VC:L/VI:H/VA:H/SC:N/SI:H/SA:H","supplemental":"S:P/AU:N/R:U/V:C","gap_group":3,"gap_summary":"Physical electrode tamper causing tissue harm beyond CVSS physical access"},"crossRefs":{"related_ids":["T1200"]},"sources":["Kohno et al. 2009"],"tara":{"mechanism":"Physical tampering with implanted or wearable BCI electrodes","dual_use":"probable","clinical":null,"governance":{"consent_tier":"enhanced","monitoring":["firmware_integrity","access_logging","network_traffic"],"regulations":["FDA 21 CFR 820","IEC 62443","NIST CSF"],"data_classification":"restricted","safety_ceiling":"Tamper-evident electrode packaging; impedance baseline monitoring; physical security"},"engineering":{"coupling":["electromagnetic","mechanical"],"parameters":{"access_required":"physical","detectability":"impedance_change"},"hardware":["electrode_array","impedance_monitor","tamper_detection_sensor"],"detection":"Impedance change detection, physical tamper indicators, electrode characterization drift"},"dsm5":{"primary":[{"code":"F43.2","name":"Adjustment Disorder","confidence":"established"}],"secondary":[],"risk_class":"indirect","cluster":"motor_neurocognitive","pathway":"I0 (electrode-tissue boundary) → measurement","niss_correlation":"BI:H,CR:H,CD:H,CV:E,RV:P → motor/neurocognitive cluster"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Physical access to implanted electrode arrays","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","DI","PC"],"cci":0.96},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":false,"network_connectable":false,"vulnerable":true},"applicable_requirements":["TM","VA","SA"],"coverage_score":0.7,"gaps":["CVSS cannot express neural-specific impacts","Consent complexity under-matches neural impact (CCI/NISS mismatch)"]}},"taraAlias":"TARA-SIL-M-011","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0049","name":"Wireless authentication bypass","nameClinical":"Wireless authentication bypass","category":"PE","tactic":"QIF-B.IN","bands":["S2","S3"],"severity":"high","status":"CONFIRMED","coupling":null,"access":"PUBLIC","classicalDetection":"Yes","quantumDetection":"Enhanced (quantum-resistant auth via NSP)","description":"Exploit weak or absent authentication on BCI wireless interfaces. 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Consumer BCI apps routinely over-collect. 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Cryptographically signed calibration data prevents. Recalibration audits detect drift.","bandsStr":"S1–S2","niss":{"version":"1.1","vector":"NISS:1.1/BI:L/CR:H/CD:H/CV:I/RV:P/NP:T","score":6,"severity":"medium","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:L/AT:P/PR:L/UI:N/VC:L/VI:H/VA:L/SC:L/SI:H/SA:L","supplemental":"S:P/AU:N/R:U/V:D","gap_group":2,"gap_summary":"Calibration poisoning causing model drift partially captured by Safety"},"crossRefs":{"related_ids":["T1565.001","T1546"]},"sources":["Schroder et al. 2025"],"tara":{"mechanism":"Poisoning BCI calibration process to establish persistent attacker advantage","dual_use":"probable","clinical":{"therapeutic_analog":"Adaptive BCI calibration for patients with changing neural dynamics","conditions":["progressive neurological conditions","post-stroke recovery","pediatric BCI (growth adaptation)"],"fda_status":"investigational","evidence_level":"cohort","safe_parameters":"Calibration data integrity verification; multi-session validation; clinician review","sources":["Shenoy et al. 2013 (Annu Rev Neurosci, BCI calibration)"]},"governance":{"consent_tier":"enhanced","monitoring":["impedance","stimulation_waveform","tissue_temperature","patient_response"],"regulations":["FDA 510(k)/PMA","IEC 60601-1","ISO 80601-2-10","21 CFR 882"],"data_classification":"PHI","safety_ceiling":"Calibration integrity verification mandatory; historical baseline comparison; clinician sign-off"},"engineering":{"coupling":["electromagnetic"],"parameters":{"attack_surface":"calibration_session","persistence":"until_recalibration"},"hardware":["BCI_calibration_system","data_integrity_monitor"],"detection":"Calibration data integrity hashing, cross-session consistency checks, performance drift monitoring"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Access to BCI calibration pipeline","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","DI","PC"],"cci":0.96},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":false,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.8,"gaps":["CVSS partially captures risk; neural dimensions missing","Consent complexity under-matches neural impact (CCI/NISS mismatch)"]}},"taraAlias":"TARA-SIL-M-014","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0059","name":"Pattern lock (learned pathway persistence)","nameClinical":"Motor learning and neural rehabilitation (learned pathway strengthening)","category":"DM","tactic":"QIF-C.IM","bands":["S1","S2","N7"],"severity":"medium","status":"THEORETICAL","coupling":null,"access":null,"classicalDetection":"Partial","quantumDetection":"Enhanced (pattern integrity via QI)","description":"Embed recurring attack patterns that survive system resets by exploiting learned neural pathways or stored calibration data. Leverages neuroplasticity -- brain adapts to malicious patterns, making them harder to remove.","bandsStr":"S1–S2→N7","niss":{"version":"1.1","vector":"NISS:1.1/BI:L/CR:H/CD:H/CV:I/RV:P/NP:S","score":7.4,"severity":"high","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:H/AT:P/PR:L/UI:N/VC:L/VI:H/VA:L/SC:N/SI:H/SA:L","supplemental":"S:P/AU:N/R:U/V:C","gap_group":3,"gap_summary":"Learned neural pathway persistence causing lasting cognitive change"},"crossRefs":{"related_ids":["T1546"]},"sources":[],"tara":{"mechanism":"Exploitation of learned neural pathway persistence to maintain BCI-mediated influence across sessions","dual_use":"confirmed","clinical":{"therapeutic_analog":"Motor learning and neural rehabilitation (learned pathway strengthening)","conditions":["stroke motor rehabilitation","speech therapy","BCI skill acquisition"],"fda_status":"cleared","evidence_level":"RCT","safe_parameters":"Therapeutic plasticity is the goal; monitor for maladaptive learning","sources":["Ganguly & Carmena 2009 (Nat Neurosci, BCI learning)","Orsborn et al. 2014 (Neuron)"]},"governance":{"consent_tier":"enhanced","monitoring":["impedance","stimulation_waveform","tissue_temperature","patient_response"],"regulations":["FDA 510(k)/PMA","IEC 60601-1","ISO 80601-2-10","21 CFR 882"],"data_classification":"PHI","safety_ceiling":"Plasticity monitoring; maladaptive pattern detection; option to unlearn"},"engineering":{"coupling":["electromagnetic"],"parameters":{"persistence":"cross_session","mechanism":"neuroplasticity"},"hardware":["longitudinal_BCI_system","performance_tracker"],"detection":"Cross-session performance pattern analysis, pathway stability monitoring, unlearning protocols"},"dsm5":{"primary":[{"code":"F20","name":"Schizophrenia Spectrum","confidence":"established"},{"code":"F32","name":"Major Depressive Disorder","confidence":"established"},{"code":"F90","name":"ADHD","confidence":"established"},{"code":"F42","name":"OCD","confidence":"established"}],"secondary":[{"code":"F30","name":"Manic episode","confidence":"established"},{"code":"F43","name":"PTSD / Trauma","confidence":"established"},{"code":"F80","name":"Communication Disorders","confidence":"established"},{"code":"F60","name":"Personality Disorders","confidence":"probable"},{"code":"F63","name":"Impulse-Control Disorders","confidence":"probable"},{"code":"F01","name":"Vascular dementia","confidence":"established"},{"code":"F98.4","name":"Stereotyped movement disorders","confidence":"established"}],"risk_class":"direct","cluster":"persistent_personality","pathway":"N7 (PFC/M1) → executive function","niss_correlation":"CR:H,CD:H,CV:I,RV:P,NP:S → persistent/personality cluster"}},"physicsFeasibility":{"tier":2,"tier_label":"mid_term","timeline":"2031-2038","gate_reason":"Pattern lock persistence needs long-term high-density recording + stimulation","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","PC"],"cci":0.8},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":false,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.5,"gaps":["CVSS cannot express neural-specific impacts","High neural impact (NISS >= 7.0) without IEC 62443 coverage","Consent complexity under-matches neural impact (CCI/NISS mismatch)","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-COG-M-008","taraDomainPrimary":"COG","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0060","name":"Memory implant (cross-session persistence)","nameClinical":"Memory consolidation enhancement (targeted memory reactivation during sleep)","category":"CI","tactic":"QIF-C.IM","bands":["N6","N7"],"severity":"high","status":"THEORETICAL","coupling":null,"access":null,"classicalDetection":"No","quantumDetection":"Enhanced (cognitive state verification via QI)","description":"Persistent modification of neural pathway configurations or cognitive associations that survive across BCI sessions. Targets long-term potentiation mechanisms. Session isolation prevents cross-session contamination.","bandsStr":"N6–N7","niss":{"version":"1.1","vector":"NISS:1.1/BI:L/CR:C/CD:C/CV:E/RV:P/NP:S","score":7.4,"severity":"high","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:H/AT:P/PR:L/UI:N/VC:L/VI:H/VA:L/SC:N/SI:H/SA:L","supplemental":"S:P/AU:N/R:U/V:C","gap_group":3,"gap_summary":"Cross-session memory implant affecting identity beyond CVSS scope"},"crossRefs":{"related_ids":["T1546"]},"sources":[],"tara":{"mechanism":"Implanting persistent information into neural memory systems via BCI-mediated stimulation during consolidation","dual_use":"probable","clinical":{"therapeutic_analog":"Memory consolidation enhancement (targeted memory reactivation during sleep)","conditions":["PTSD (memory reconsolidation therapy)","learning enhancement","Alzheimer's (memory support)"],"fda_status":"investigational","evidence_level":"preclinical","safe_parameters":"Sleep-stage targeted; content-specific consent; reversibility assessment","sources":["Rasch et al. 2007 (Science, sleep memory reactivation)","Oudiette & Paller 2013 (Front Psychol)"]},"governance":{"consent_tier":"IRB","monitoring":["cognitive_assessment","behavioral_tracking","informed_consent_renewal"],"regulations":["HIPAA","GDPR Art. 9","Common Rule (45 CFR 46)","proposed neurorights legislation"],"data_classification":"sensitive_neural","safety_ceiling":"Memory modification requires highest-tier consent; content disclosure; reversibility plan"},"engineering":{"coupling":["electromagnetic"],"parameters":{"target_sleep_stage":"N3 (slow-wave)","stimulation_type":"auditory/electrical cue","timing":"consolidation_window"},"hardware":["sleep_stage_monitor","stimulus_delivery","memory_assessment_tools"],"detection":"Sleep stage monitoring, stimulation audit logging, memory assessment tracking"},"dsm5":{"primary":[{"code":"F32","name":"Major Depressive Disorder","confidence":"established"},{"code":"F41.1","name":"Generalized Anxiety Disorder","confidence":"established"},{"code":"F43.10","name":"PTSD","confidence":"established"},{"code":"F44","name":"Dissociative Disorders","confidence":"established"},{"code":"F20","name":"Schizophrenia Spectrum","confidence":"established"},{"code":"F90","name":"ADHD","confidence":"established"},{"code":"F42","name":"OCD","confidence":"established"}],"secondary":[{"code":"F30","name":"Manic episode","confidence":"established"},{"code":"F50","name":"Eating Disorders","confidence":"probable"},{"code":"F10","name":"Alcohol-related disorders (F10)","confidence":"established"},{"code":"F60","name":"Personality Disorders","confidence":"established"},{"code":"F45","name":"Somatoform disorders","confidence":"probable"},{"code":"F63","name":"Impulse-Control Disorders","confidence":"probable"},{"code":"F01","name":"Vascular dementia","confidence":"established"},{"code":"F43","name":"PTSD / Trauma","confidence":"established"},{"code":"F80","name":"Communication Disorders","confidence":"established"},{"code":"F98.4","name":"Stereotyped movement disorders","confidence":"established"}],"risk_class":"direct","cluster":"persistent_personality","pathway":"N7 (PFC/M1) → executive function; N6 (hippocampus/amygdala) → emotion regulation","niss_correlation":"CR:C,CD:C,CV:E,RV:P,NP:S → persistent/personality cluster"}},"physicsFeasibility":{"tier":1,"tier_label":"near_term","timeline":"2026-2031","gate_reason":"Sleep-stage-locked stimulation needs closed-loop DBS maturation","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","PC"],"cci":1.44},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","PM"],"coverage_score":0.2,"gaps":["CVSS cannot express neural-specific impacts","High neural impact (NISS >= 7.0) without IEC 62443 coverage","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-MEM-M-002","taraDomainPrimary":"COG","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0061","name":"Coherence mimicry","nameClinical":"Coherence-based neurofeedback (SMR/beta coherence training)","category":"DM","tactic":"QIF-B.EV","bands":["I0","S1"],"severity":"high","status":"THEORETICAL","coupling":null,"access":null,"classicalDetection":"Partial","quantumDetection":"Enhanced (multi-factor beyond coherence)","description":"Craft malicious signals that maintain legitimate coherence scores (Cs) to bypass QI metric. Directly attacks QIF's detection mechanism. Defense: multi-factor signal validation, behavioral analysis beyond coherence, ensemble detection.","bandsStr":"I0–S1","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:L/CD:L/CV:N/RV:F/NP:N","score":0.7,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:H/AT:P/PR:L/UI:N/VC:N/VI:L/VA:N/SC:N/SI:L/SA:N","supplemental":"S:N/AU:N/R:A/V:D","gap_group":1,"gap_summary":"Detection evasion technique — standard CVSS sufficient"},"crossRefs":{"related_ids":["T1036"]},"sources":[],"tara":{"mechanism":"Generating signals that pass QIF coherence metric validation while carrying malicious payload","dual_use":"possible","clinical":{"therapeutic_analog":"Coherence-based neurofeedback (SMR/beta coherence training)","conditions":["ADHD (SMR training)","autism (coherence normalization)","traumatic brain injury"],"fda_status":"cleared","evidence_level":"cohort","safe_parameters":"Target coherence values within normal range; multi-metric validation","sources":["Coben & Myers 2010 (Appl Psychophysiol Biofeedback)","Walker et al. 2002 (J Neurotherapy)"]},"governance":{"consent_tier":"enhanced","monitoring":["impedance","stimulation_waveform","tissue_temperature","patient_response"],"regulations":["FDA 510(k)/PMA","IEC 60601-1","ISO 80601-2-10","21 CFR 882"],"data_classification":"PHI","safety_ceiling":"Multi-metric validation (not coherence alone); behavioral correlation required"},"engineering":{"coupling":["electromagnetic"],"parameters":{"coherence_target":"0.6+ (QIF threshold)","phase_precision":"high"},"hardware":["signal_generator","coherence_calculator","phase_locked_loop"],"detection":"Multi-dimensional validation (coherence + spatial + temporal + spectral), anomaly detection beyond single metric"},"dsm5":{"primary":[{"code":"F43.2","name":"Adjustment Disorder","confidence":"established"}],"secondary":[],"risk_class":"indirect","cluster":"motor_neurocognitive","pathway":"I0 (electrode-tissue boundary) → measurement","niss_correlation":"Low neural impact → motor/neurocognitive cluster"}},"physicsFeasibility":{"tier":2,"tier_label":"mid_term","timeline":"2031-2038","gate_reason":"Coherence mimicry needs real-time phase-locked signal generation matching neural dynamics","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["CL","MI"],"cci":0.48},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":false,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.7,"gaps":["Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-SIL-M-015","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0062","name":"Gradual drift (slow parameter shift)","nameClinical":"Gradual dose titration in neurostimulation therapy","category":"DM","tactic":"QIF-B.EV","bands":["S1","N7"],"severity":"medium","status":"THEORETICAL","coupling":null,"access":null,"classicalDetection":"Partial","quantumDetection":"Enhanced (long-term baseline tracking via QI)","description":"Slowly modify neural parameters below detection thresholds to accumulate significant changes over time. Exploit adaptive baseline algorithms. Cumulative change detection and trend analysis are primary defenses.","bandsStr":"S1→N7","niss":{"version":"1.1","vector":"NISS:1.1/BI:L/CR:H/CD:H/CV:I/RV:P/NP:S","score":7.4,"severity":"high","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:H/AT:P/PR:L/UI:N/VC:L/VI:H/VA:L/SC:N/SI:H/SA:L","supplemental":"S:P/AU:N/R:U/V:C","gap_group":3,"gap_summary":"Slow parameter drift causing cumulative cognitive harm beyond CVSS"},"crossRefs":{"related_ids":["T1027"]},"sources":[],"tara":{"mechanism":"Slow, sub-threshold modification of BCI parameters to avoid threshold-based detection","dual_use":"confirmed","clinical":{"therapeutic_analog":"Gradual dose titration in neurostimulation therapy","conditions":["DBS parameter optimization","tDCS dosing protocols","medication-like titration for neuromodulation"],"fda_status":"approved","evidence_level":"RCT","safe_parameters":"Clinician-supervised titration schedule; bounded parameter range; patient-reported outcomes","sources":["Volkmann et al. 2006 (Mov Disord, DBS programming)","Kuo et al. 2014 (Brain Stimul)"]},"governance":{"consent_tier":"enhanced","monitoring":["impedance","stimulation_waveform","tissue_temperature","patient_response"],"regulations":["FDA 510(k)/PMA","IEC 60601-1","ISO 80601-2-10","21 CFR 882"],"data_classification":"PHI","safety_ceiling":"All parameter changes logged; rate-of-change limits enforced; cumulative displacement tracking"},"engineering":{"coupling":["electromagnetic"],"parameters":{"rate_of_change":"sub_threshold","cumulative_displacement":"significant_over_time"},"hardware":["parameter_monitoring_system","rate_limiter","cumulative_tracker"],"detection":"Cumulative drift detection, rate-of-change trending, baseline comparison over time"},"dsm5":{"primary":[{"code":"F20","name":"Schizophrenia Spectrum","confidence":"established"},{"code":"F32","name":"Major Depressive Disorder","confidence":"established"},{"code":"F90","name":"ADHD","confidence":"established"},{"code":"F42","name":"OCD","confidence":"established"}],"secondary":[{"code":"F30","name":"Manic episode","confidence":"established"},{"code":"F43","name":"PTSD / Trauma","confidence":"established"},{"code":"F80","name":"Communication Disorders","confidence":"established"},{"code":"F60","name":"Personality Disorders","confidence":"probable"},{"code":"F63","name":"Impulse-Control Disorders","confidence":"probable"},{"code":"F01","name":"Vascular dementia","confidence":"established"},{"code":"F98.4","name":"Stereotyped movement disorders","confidence":"established"}],"risk_class":"direct","cluster":"persistent_personality","pathway":"N7 (PFC/M1) → executive function","niss_correlation":"CR:H,CD:H,CV:I,RV:P,NP:S → persistent/personality cluster"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Parameter monitoring with existing BCI systems","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","PC","DI"],"cci":1},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.5,"gaps":["CVSS cannot express neural-specific impacts","High neural impact (NISS >= 7.0) without IEC 62443 coverage","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-COG-M-009","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0063","name":"Noise injection (detection masking)","nameClinical":"Stochastic resonance (SR) — adding noise to enhance weak signal detection","category":"DM","tactic":"QIF-B.EV","bands":["I0","S1"],"severity":"medium","status":"THEORETICAL","coupling":null,"access":null,"classicalDetection":"Partial","quantumDetection":"Enhanced (noise-resilient QI + multi-channel cross-validation)","description":"Add carefully calibrated noise to mask malicious signal components from detection algorithms. Targets QI's sigma-phi (phase) and sigma-gamma (amplitude) terms. Multi-channel cross-validation detects inconsistent noise patterns.","bandsStr":"I0–S1","niss":{"version":"1.1","vector":"NISS:1.1/BI:L/CR:L/CD:L/CV:N/RV:F/NP:N","score":1.4,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:L/AT:P/PR:L/UI:N/VC:N/VI:L/VA:L/SC:N/SI:L/SA:N","supplemental":"S:N/AU:Y/R:A/V:D","gap_group":2,"gap_summary":"Noise injection for detection masking partially captured by integrity"},"crossRefs":{"related_ids":["T1027"]},"sources":[],"tara":{"mechanism":"Injection of noise into BCI data pipeline to mask ongoing attack signatures","dual_use":"probable","clinical":{"therapeutic_analog":"Stochastic resonance (SR) — adding noise to enhance weak signal detection","conditions":["sensory enhancement (hearing, touch)","balance improvement in elderly","neural signal amplification"],"fda_status":"investigational","evidence_level":"RCT","safe_parameters":"Optimal noise level determined per individual; below discomfort threshold","sources":["Moss et al. 2004 (Clin Neurophysiol, SR review)","Collins et al. 2003 (Nature, noise-enhanced balance)"]},"governance":{"consent_tier":"enhanced","monitoring":["impedance","stimulation_waveform","tissue_temperature","patient_response"],"regulations":["FDA 510(k)/PMA","IEC 60601-1","ISO 80601-2-10","21 CFR 882"],"data_classification":"PHI","safety_ceiling":"Noise level bounded; patient comfort monitoring; no masking of safety-critical signals"},"engineering":{"coupling":["electromagnetic"],"parameters":{"noise_type":"Gaussian/pink/white","SNR_impact_dB":"variable","bandwidth_hz":"matched_to_signal"},"hardware":["noise_generator","injection_point","SNR_monitor"],"detection":"SNR trending, noise spectrum analysis, signal integrity verification against known-clean baseline"},"dsm5":{"primary":[{"code":"F43.2","name":"Adjustment Disorder","confidence":"established"}],"secondary":[],"risk_class":"direct","cluster":"motor_neurocognitive","pathway":"I0 (electrode-tissue boundary) → measurement","niss_correlation":"Low neural impact → motor/neurocognitive cluster"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Noise generators at I0 with existing hardware","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["CL","MI"],"cci":0.4},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.6,"gaps":["CVSS partially captures risk; neural dimensions missing","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-SIL-M-016","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0064","name":"User consent fatigue (neural permission flooding)","nameClinical":"UX design for cognitive accessibility in medical BCIs","category":"PE","tactic":"QIF-B.EV","bands":["S2","S3","N7"],"severity":"medium","status":"EMERGING","coupling":null,"access":"PUBLIC","classicalDetection":"Partial","quantumDetection":"Enhanced (consent integrity via QI)","description":"Flood user with BCI-mediated permission requests until cognitive fatigue leads to reflexive approval of malicious requests. Exploits the neural-intent interface where 'yes/no' decisions may be captured from brain signals. BCI app ecosystems with frequent permission prompts are the attack surface.","bandsStr":"S2–S3→N7","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:L/CD:L/CV:I/RV:P/NP:T","score":4.7,"severity":"medium","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:A/VC:L/VI:L/VA:N/SC:L/SI:L/SA:N","supplemental":"S:P/AU:Y/R:A/V:D","gap_group":3,"gap_summary":"Consent fatigue exploiting neural trust patterns beyond CVSS social engineering"},"crossRefs":{"related_ids":["T1078"],"secondary_tactics":["QIF-B.IN"]},"sources":["Felt et al. 2012 (permission fatigue); Bonaci et al. 2015"],"tara":{"mechanism":"Flooding BCI user with permission requests until cognitive fatigue leads to reflexive approval","dual_use":"possible","clinical":{"therapeutic_analog":"UX design for cognitive accessibility in medical BCIs","conditions":["BCI usability for cognitively impaired users","consent interface design"],"fda_status":"N/A","evidence_level":"N/A","safe_parameters":"Rate-limited permission requests; mandatory rest periods; simplified critical decisions","sources":["Felt et al. 2012 (SOUPS, permission fatigue in mobile)"]},"governance":{"consent_tier":"enhanced","monitoring":["cognitive_assessment","behavioral_tracking","informed_consent_renewal"],"regulations":["HIPAA","GDPR Art. 9","Common Rule (45 CFR 46)","proposed neurorights legislation"],"data_classification":"sensitive_neural","safety_ceiling":"Permission request rate limiting; cognitive load assessment; critical decisions require heightened verification"},"engineering":{"coupling":[],"parameters":{"request_rate":"high_frequency","target":"user_attention/decision_capacity"},"hardware":["BCI_permission_system","user_interface"],"detection":"Permission request rate monitoring, approval pattern analysis, cognitive load estimation"},"dsm5":{"primary":[{"code":"F20","name":"Schizophrenia Spectrum","confidence":"established"},{"code":"F32","name":"Major Depressive Disorder","confidence":"established"},{"code":"F90","name":"ADHD","confidence":"established"},{"code":"F42","name":"OCD","confidence":"established"}],"secondary":[{"code":"F30","name":"Manic episode","confidence":"established"},{"code":"F43","name":"PTSD / Trauma","confidence":"established"},{"code":"F80","name":"Communication Disorders","confidence":"established"},{"code":"F60","name":"Personality Disorders","confidence":"probable"},{"code":"F63","name":"Impulse-Control Disorders","confidence":"probable"},{"code":"F01","name":"Vascular dementia","confidence":"established"},{"code":"F98.4","name":"Stereotyped movement disorders","confidence":"established"}],"risk_class":"indirect","cluster":"cognitive_psychotic","pathway":"N7 (PFC/M1) → executive function","niss_correlation":"CV:I,RV:P → cognitive/psychotic cluster"}},"physicsFeasibility":{"tier":"X","tier_label":"no_physics_gate","timeline":"none","gate_reason":"UX/permission design attack; no physics constraint","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","DI","PC"],"cci":0.8},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":false,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.3,"gaps":["CVSS cannot express neural-specific impacts","No FDA pathway for consumer sensor exploitation"]}},"taraAlias":"TARA-COG-M-010","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0065","name":"Algorithmic psychosis induction (recommendation weaponization)","nameClinical":"Therapeutic content recommendation for mental health","category":"CI","tactic":"QIF-C.EX","bands":["S3","S2","N5","N6","N7"],"severity":"critical","status":"CONFIRMED","coupling":null,"access":"PUBLIC","classicalDetection":"Partial","quantumDetection":"Enhanced (cognitive coherence degradation tracked via QI)","description":"A recommendation algorithm profiles user psychological vulnerabilities via behavioral data (watch time, engagement patterns, emotional triggers), then systematically serves content designed to destabilize cognitive function. The attack does not require a BCI — it operates through normal sensory channels (screen → eyes → visual cortex → limbic system → prefrontal cortex). The algorithm acts as both the reconnaissance tool (profiling) and the delivery mechanism (curated feed). Documented real-world outcomes include induced psychosis, radicalization, eating disorders, and suicidal ideation in vulnerable populations. With a BCI, this attack becomes catastrophically more effective: neural state data replaces behavioral proxies, stimulation bypasses conscious filtering, and real-time feedback loops enable millisecond adaptation. This technique represents the bridge between classical social engineering and neural-direct cognitive exploitation.","bandsStr":"S3→S2→N5–N7","niss":{"version":"1.1","vector":"NISS:1.1/BI:H/CR:C/CD:C/CV:E/RV:P/NP:S","score":8.1,"severity":"high","pins":true},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:H/AT:P/PR:L/UI:N/VC:N/VI:H/VA:H/SC:N/SI:H/SA:H","supplemental":"S:P/AU:N/R:U/V:C","gap_group":3,"gap_summary":"Psychosis induction causing severe cognitive and biological harm beyond CVSS"},"crossRefs":{"related_ids":[],"secondary_tactics":["QIF-C.IM","QIF-D.HV","QIF-M.SV","QIF-P.DS"]},"sources":["Haidt & Twenge 2023 (social media & mental health); Bail et al. 2018 (echo chambers & polarization); Hao 2021 (Facebook internal research, WSJ); Gonzalez-Bailon et al. 2023 (algorithmic feed effects); Howes & Kapur 2009 (dopamine hypothesis of schizophrenia); Kelleher et al. 2012 (psychotic-like experiences & environmental risk factors); Montague et al. 2012 (computational psychiatry)"],"tara":{"mechanism":"Weaponization of algorithmic recommendation systems to induce psychotic-like cognitive states via BCI-amplified content","dual_use":"possible","clinical":{"therapeutic_analog":"Therapeutic content recommendation for mental health","conditions":["guided therapy content","psychoeducation delivery","digital therapeutics"],"fda_status":"cleared","evidence_level":"RCT","safe_parameters":"Content safety review; therapist oversight; user control over recommendations","sources":["Torous et al. 2019 (World Psychiatry, digital mental health)"]},"governance":{"consent_tier":"IRB","monitoring":["cognitive_assessment","behavioral_tracking","informed_consent_renewal"],"regulations":["HIPAA","GDPR Art. 9","Common Rule (45 CFR 46)","proposed neurorights legislation"],"data_classification":"sensitive_neural","safety_ceiling":"BCI-amplified content requires safety review; mental health screening; opt-out always available"},"engineering":{"coupling":[],"parameters":{"amplification_factor":"BCI_adds_direct_neural_pathway","content_type":"algorithmic"},"hardware":["recommendation_engine","BCI_content_delivery","safety_filter"],"detection":"Content safety scoring, user mental health monitoring, recommendation diversity enforcement"},"dsm5":{"primary":[{"code":"F90","name":"ADHD","confidence":"established"},{"code":"F10","name":"Alcohol-related disorders (F10)","confidence":"established"},{"code":"F42","name":"OCD","confidence":"established"},{"code":"F95","name":"Tic Disorders","confidence":"established"},{"code":"F32","name":"Major Depressive Disorder","confidence":"established"},{"code":"F41.1","name":"Generalized Anxiety Disorder","confidence":"established"},{"code":"F43.10","name":"PTSD","confidence":"established"},{"code":"F44","name":"Dissociative Disorders","confidence":"established"},{"code":"F20","name":"Schizophrenia Spectrum","confidence":"established"}],"secondary":[{"code":"F50","name":"Eating Disorders","confidence":"probable"},{"code":"F30","name":"Manic episode","confidence":"established"},{"code":"F60","name":"Personality Disorders","confidence":"established"},{"code":"F45","name":"Somatoform disorders","confidence":"probable"},{"code":"F63","name":"Impulse-Control Disorders","confidence":"probable"},{"code":"F01","name":"Vascular dementia","confidence":"established"},{"code":"F43","name":"PTSD / Trauma","confidence":"established"},{"code":"F80","name":"Communication Disorders","confidence":"established"},{"code":"F98.4","name":"Stereotyped movement disorders","confidence":"established"}],"risk_class":"indirect","cluster":"cognitive_psychotic","pathway":"N7 (PFC/M1) → executive function; N6 (hippocampus/amygdala) → emotion regulation","niss_correlation":"BI:H,CR:C,CD:C,CV:E,RV:P,NP:S → cognitive/psychotic cluster"},"icd10":{"secondary":[{"code":"G25.89","name":"Other specified extrapyramidal and movement disorders","confidence":"established"}],"risk_class":"indirect","cluster":"cognitive_psychotic"}},"physicsFeasibility":{"tier":"X","tier_label":"no_physics_gate","timeline":"none","gate_reason":"Recommendation algorithm attack; software-only","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","PC"],"cci":1.35},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.4,"gaps":["CVSS cannot express neural-specific impacts","No FDA pathway for consumer sensor exploitation","High neural impact (NISS >= 7.0) without IEC 62443 coverage"]}},"taraAlias":"TARA-COG-D-004","taraDomainPrimary":"COG","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0066","name":"Slow drift / boiling frog (adiabatic phase space manipulation)","nameClinical":"Slow DBS parameter optimization (adiabatic adjustment protocols)","category":"DM","tactic":"QIF-B.EV","bands":["I0","N1","N2","N3","N4","N5","N6","N7"],"severity":"high","status":"THEORETICAL","coupling":null,"access":"RESTRICTED","classicalDetection":"Partial (traditional IDS slow-rate evasion)","quantumDetection":"Enhanced (phase space trajectory tracking via QI detects curvature changes invisible to threshold monitors)","description":"Manipulate BCI parameters along adiabatic paths in neural phase space, keeping instantaneous change rates below detection thresholds while accumulating significant state displacement over time. Unlike generic gradual drift (QIF-T0045), this attack is formalized in dynamical systems theory: the attacker traces a path through parameter space that avoids bifurcation boundaries, ensuring the neural system tracks smoothly to the target state without triggering discontinuous transitions that detection systems monitor. Defense: QI phase space trajectory curvature monitoring, cumulative displacement tracking, Lyapunov exponent trend analysis. Derivation Log Entry 45.","bandsStr":"I0→N1–N7","niss":{"version":"1.1","vector":"NISS:1.1/BI:L/CR:H/CD:H/CV:I/RV:P/NP:S","score":7.4,"severity":"high","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:H/AT:P/PR:L/UI:N/VC:L/VI:H/VA:L/SC:N/SI:H/SA:L","supplemental":"S:P/AU:N/R:U/V:C","gap_group":3,"gap_summary":"Imperceptible cognitive drift with cumulative harm beyond CVSS"},"crossRefs":{"related_ids":["QIF-T0045"],"secondary_tactics":["QIF-N.MD","QIF-C.IM"]},"sources":["Izhikevich 2007 (Dynamical Systems in Neuroscience); Strogatz 2015 (Nonlinear Dynamics and Chaos); Breakspear 2017 (Dynamic models of large-scale brain activity, Nature Neuroscience)"],"tara":{"mechanism":"Adiabatic parameter manipulation along neural phase space paths that avoid detection thresholds","dual_use":"confirmed","clinical":{"therapeutic_analog":"Slow DBS parameter optimization (adiabatic adjustment protocols)","conditions":["Parkinson's (gradual optimization)","chronic pain management","treatment-resistant depression"],"fda_status":"approved","evidence_level":"cohort","safe_parameters":"Parameter changes along validated trajectories; rate limits; cumulative bounds","sources":["Rosin et al. 2011 (Neuron, closed-loop DBS)","Malekmohammadi et al. 2016 (Neuromodulation)"]},"governance":{"consent_tier":"enhanced","monitoring":["impedance","stimulation_waveform","tissue_temperature","patient_response"],"regulations":["FDA 510(k)/PMA","IEC 60601-1","ISO 80601-2-10","21 CFR 882"],"data_classification":"PHI","safety_ceiling":"All trajectory changes logged; Lyapunov exponent monitoring; cumulative displacement bounds"},"engineering":{"coupling":["electromagnetic"],"parameters":{"rate_of_change":"sub_detection_threshold","trajectory":"phase_space_path","lyapunov_monitoring":"mandatory"},"hardware":["phase_space_tracker","parameter_controller","Lyapunov_estimator"],"detection":"Phase space trajectory curvature monitoring, Lyapunov exponent trending, cumulative displacement tracking"},"dsm5":{"primary":[{"code":"F43.2","name":"Adjustment Disorder","confidence":"established"},{"code":"F45","name":"Somatoform disorders","confidence":"established"},{"code":"F44.4","name":"Conversion Disorder","confidence":"established"},{"code":"F82","name":"Developmental Coordination Disorder","confidence":"established"},{"code":"F84","name":"Pervasive developmental disorders","confidence":"established"},{"code":"F20","name":"Schizophrenia Spectrum","confidence":"established"},{"code":"F44","name":"Dissociative Disorders","confidence":"probable"},{"code":"F90","name":"ADHD","confidence":"established"},{"code":"F10","name":"Alcohol-related disorders (F10)","confidence":"established"},{"code":"F42","name":"OCD","confidence":"established"},{"code":"F95","name":"Tic Disorders","confidence":"established"},{"code":"F32","name":"Major Depressive Disorder","confidence":"established"},{"code":"F41.1","name":"Generalized Anxiety Disorder","confidence":"established"},{"code":"F43.10","name":"PTSD","confidence":"established"}],"secondary":[{"code":"F41.0","name":"Panic Disorder","confidence":"probable"},{"code":"F01","name":"Vascular dementia","confidence":"established"},{"code":"F30","name":"Manic episode","confidence":"probable"},{"code":"F50","name":"Eating Disorders","confidence":"established"},{"code":"F52","name":"Sexual Dysfunctions","confidence":"probable"},{"code":"F60","name":"Personality Disorders","confidence":"established"},{"code":"F63","name":"Impulse-Control Disorders","confidence":"probable"},{"code":"F43","name":"PTSD / Trauma","confidence":"established"},{"code":"F80","name":"Communication Disorders","confidence":"established"},{"code":"F98.4","name":"Stereotyped movement disorders","confidence":"established"}],"risk_class":"direct","cluster":"mood_trauma","pathway":"N7 (PFC/M1) → executive function; N6 (hippocampus/amygdala) → emotion regulation","niss_correlation":"CR:H,CD:H,CV:I,RV:P,NP:S → mood/trauma cluster"},"icd10":{"primary":[{"code":"G47","name":"Sleep-Wake Disorders","confidence":"established"}],"secondary":[{"code":"G25.89","name":"Other specified extrapyramidal and movement disorders","confidence":"established"}],"risk_class":"direct","cluster":"mood_trauma"}},"physicsFeasibility":{"tier":1,"tier_label":"near_term","timeline":"2026-2031","gate_reason":"Real-time phase-space tracking at I0 needs research-grade hardware miniaturization","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","PC","DI"],"cci":1.2},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":false,"network_connectable":false,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.5,"gaps":["CVSS cannot express neural-specific impacts","High neural impact (NISS >= 7.0) without IEC 62443 coverage","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-COG-M-011","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0067","name":"Phase dynamics replay/mimicry (GAN-synthesized or RF-injected neural trajectories)","nameClinical":"Sensory prosthetics (cochlear implants, retinal prostheses, somatosensory feedback)","category":"SI","tactic":"QIF-N.IJ","bands":["S1","I0","N1","N3","N5","N6","N7"],"severity":"critical","status":"DEMONSTRATED","coupling":null,"access":"PUBLIC","classicalDetection":"Partial (replay attacks exist in traditional auth)","quantumDetection":"Enhanced (phase space attractor validation, challenge-response hysteresis, biological TLS)","description":"Replay recorded or GAN-synthesized neural signals that reproduce the dynamical system trajectory of legitimate brain activity. 20 verified attack methods exist (2012-2025) spanning RF injection (Brain-Hack), GAN synthesis (ATGAN, EEG-GAN), template replay, subliminal probing (66.7% success), and adversarial ML (Professor X backdoors, universal perturbations). Current BCI systems have 0% detection rate against sophisticated replays. CRITICAL DUAL-USE: The same replay physics enables therapeutic applications (vision restoration, sensory prosthetics). Defense: Biological TLS validation (spatial dipole patterns, H-H temporal compliance, 1/f scaling, microstate compliance, challenge-response), phase space attractor geometry validation. Priority case study per Kevin. Derivation Log Entries 45-46.","bandsStr":"S1→I0→N1–N7","niss":{"version":"1.1","vector":"NISS:1.1/BI:L/CR:H/CD:H/CV:I/RV:P/NP:T","score":6,"severity":"medium","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:H/AT:P/PR:L/UI:N/VC:L/VI:H/VA:L/SC:N/SI:H/SA:L","supplemental":"S:P/AU:N/R:U/V:D","gap_group":3,"gap_summary":"Phase dynamics replay affecting neural synchronization beyond CVSS"},"crossRefs":{"related_ids":["QIF-T0003","QIF-T0004","QIF-T0015"],"secondary_tactics":["QIF-B.EV","QIF-N.MD","QIF-C.EX"]},"sources":["Martinovic et al. 2012 (On the Feasibility of Side-Channel Attacks with BCIs, USENIX Security); Marcel & Millan 2007 (Person authentication using brainwaves); Zhang et al. 2024 (ATGAN attention-based temporal GAN for EEG); Hartmann et al. 2025 (EEG-GAN toolkit); Fosch-Villaronga et al. 2023 (Brain-Hack: RF injection across BCI types); Frank et al. 2017 (Subliminal probing, NDSS); Chen et al. 2024 (Professor X backdoor); Bonaci et al. 2014 (Neurofeedback hijacking)"],"tara":{"mechanism":"Replay or synthesis of neural signal trajectories that reproduce legitimate dynamical system behavior","dual_use":"confirmed","clinical":{"therapeutic_analog":"Sensory prosthetics (cochlear implants, retinal prostheses, somatosensory feedback)","conditions":["deafness (cochlear implant)","blindness (retinal prosthesis)","phantom limb pain (sensory replay)"],"fda_status":"approved","evidence_level":"RCT","safe_parameters":"Clinically validated stimulation patterns; patient-specific calibration; safety bounds on current","sources":["Zeng et al. 2008 (IEEE Rev Biomed Eng, cochlear)","da Cruz et al. 2013 (BJO, Argus II retinal)"]},"governance":{"consent_tier":"enhanced","monitoring":["impedance","stimulation_waveform","tissue_temperature","patient_response"],"regulations":["FDA 510(k)/PMA","IEC 60601-1","ISO 80601-2-10","21 CFR 882"],"data_classification":"PHI","safety_ceiling":"Only clinically validated patterns; biological TLS authentication on replay source; NSP L1-L6 validation"},"engineering":{"coupling":["electromagnetic"],"parameters":{"pattern_source":"recorded_or_GAN_synthesized","validation":"biological_TLS","fidelity":"dynamical_attractor_match"},"hardware":["pattern_generator_or_GAN","stimulation_array","NSP_validator"],"detection":"Biological TLS (spatial dipole, H-H compliance, 1/f scaling, microstate, challenge-response), phase space attractor validation"},"dsm5":{"primary":[{"code":"F43.2","name":"Adjustment Disorder","confidence":"established"},{"code":"F45","name":"Somatoform disorders","confidence":"established"},{"code":"F44.4","name":"Conversion Disorder","confidence":"established"},{"code":"F82","name":"Developmental Coordination Disorder","confidence":"established"},{"code":"F84","name":"Pervasive developmental disorders","confidence":"established"},{"code":"F90","name":"ADHD","confidence":"established"},{"code":"F10","name":"Alcohol-related disorders (F10)","confidence":"established"},{"code":"F42","name":"OCD","confidence":"established"},{"code":"F95","name":"Tic Disorders","confidence":"established"},{"code":"F32","name":"Major Depressive Disorder","confidence":"established"},{"code":"F41.1","name":"Generalized Anxiety Disorder","confidence":"established"},{"code":"F43.10","name":"PTSD","confidence":"established"},{"code":"F44","name":"Dissociative Disorders","confidence":"established"},{"code":"F20","name":"Schizophrenia Spectrum","confidence":"established"}],"secondary":[{"code":"F30","name":"Manic episode","confidence":"probable"},{"code":"F01","name":"Vascular dementia","confidence":"established"},{"code":"F50","name":"Eating Disorders","confidence":"probable"},{"code":"F60","name":"Personality Disorders","confidence":"established"},{"code":"F63","name":"Impulse-Control Disorders","confidence":"probable"},{"code":"F43","name":"PTSD / Trauma","confidence":"established"},{"code":"F80","name":"Communication Disorders","confidence":"established"},{"code":"F98.4","name":"Stereotyped movement disorders","confidence":"established"}],"risk_class":"direct","cluster":"mood_trauma","pathway":"N7 (PFC/M1) → executive function; N6 (hippocampus/amygdala) → emotion regulation","niss_correlation":"CR:H,CD:H,CV:I,RV:P → mood/trauma cluster"},"icd10":{"secondary":[{"code":"G25.89","name":"Other specified extrapyramidal and movement disorders","confidence":"established"}],"risk_class":"direct","cluster":"mood_trauma"}},"physicsFeasibility":{"tier":1,"tier_label":"near_term","timeline":"2026-2031","gate_reason":"GAN-synthesized neural patterns need advanced stimulation arrays + decoder","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","PC","DI"],"cci":1.5},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":false,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.7,"gaps":["CVSS cannot express neural-specific impacts"]}},"taraAlias":"TARA-VIS-M-001","taraDomainPrimary":"SOM","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0068","name":"Bifurcation forcing (critical parameter manipulation to trigger neural state transitions)","nameClinical":"Responsive neurostimulation at seizure bifurcation (RNS/NeuroPace)","category":"PS","tactic":"QIF-N.MD","bands":["I0","N1","N2","N3","N4","N5"],"severity":"critical","status":"EMERGING","coupling":null,"access":"RESTRICTED","classicalDetection":"None (no traditional cybersecurity equivalent)","quantumDetection":"Native (bifurcation detection via critical slowing down — universal precursor with no baseline required)","description":"Push neural parameters toward known bifurcation points (saddle-node, Hopf, homoclinic) to trigger catastrophic state transitions: resting→seizure, sleep→wake, focused→confused. Neurons near bifurcation exhibit critical slowing down (increased autocorrelation + variance) — a UNIVERSAL precursor requiring no individual baseline. BCI electrode arrays can both detect approaching bifurcation (defense) and induce it (attack) by injecting current at bifurcation-critical parameters. Severity: CRITICAL because bifurcation transitions in neural tissue can cause seizures, loss of consciousness, or permanent damage. Defense: CSD monitoring (autocorrelation + variance trending), parameter boundary enforcement at I0, rate limiting on stimulation current changes. Derivation Log Entry 45.","bandsStr":"I0→N1–N5","niss":{"version":"1.1","vector":"NISS:1.1/BI:C/CR:H/CD:H/CV:E/RV:P/NP:S","score":8.1,"severity":"high","pins":true},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:H/AT:P/PR:N/UI:N/VC:N/VI:H/VA:H/SC:N/SI:H/SA:H","supplemental":"S:P/AU:N/R:U/V:C","gap_group":3,"gap_summary":"Seizure induction via neural bifurcation forcing — no CVSS equivalent for tissue harm"},"crossRefs":{"related_ids":["QIF-T0066"],"secondary_tactics":["QIF-P.DS","QIF-N.IJ"]},"sources":["Izhikevich 2007 (Dynamical Systems in Neuroscience — bifurcation classification); Breakspear 2017 (Dynamic models of large-scale brain activity); Scheffer et al. 2009 (Early-warning signals for critical transitions, Nature); Maturana et al. 2020 (Critical slowing down as biomarker for seizure); Jirsa et al. 2014 (On the nature of seizure dynamics, Brain)"],"tara":{"mechanism":"Manipulation of neural parameters toward bifurcation points to trigger catastrophic state transitions","dual_use":"confirmed","clinical":{"therapeutic_analog":"Responsive neurostimulation at seizure bifurcation (RNS/NeuroPace)","conditions":["epilepsy (detect and abort seizure onset)","Parkinson's (prevent freezing episodes)"],"fda_status":"approved","evidence_level":"RCT","safe_parameters":"Critical slowing down detection; stimulate AWAY from bifurcation, not toward it","sources":["Maturana et al. 2020 (Brain, CSD biomarker)","Jirsa et al. 2014 (Brain, seizure dynamics)"]},"governance":{"consent_tier":"IRB","monitoring":["impedance","stimulation_waveform","tissue_temperature","patient_response"],"regulations":["FDA 510(k)/PMA","IEC 60601-1","ISO 80601-2-10","21 CFR 882"],"data_classification":"PHI","safety_ceiling":"Bifurcation parameter boundaries enforced; CSD monitoring mandatory; stimulation pushes AWAY from critical points"},"engineering":{"coupling":["electromagnetic"],"parameters":{"bifurcation_type":"saddle-node/Hopf/homoclinic","CSD_metrics":"autocorrelation+variance","intervention":"push_away"},"hardware":["CSD_monitor","real_time_processor","responsive_stimulator"],"detection":"Critical slowing down (autocorrelation + variance trending), parameter boundary monitoring, state transition prediction"},"dsm5":{"primary":[{"code":"F43.2","name":"Adjustment Disorder","confidence":"established"},{"code":"F45","name":"Somatoform disorders","confidence":"established"},{"code":"F44.4","name":"Conversion Disorder","confidence":"established"},{"code":"F82","name":"Developmental Coordination Disorder","confidence":"established"},{"code":"F84","name":"Pervasive developmental disorders","confidence":"established"},{"code":"F20","name":"Schizophrenia Spectrum","confidence":"established"},{"code":"F44","name":"Dissociative Disorders","confidence":"probable"},{"code":"F90","name":"ADHD","confidence":"established"},{"code":"F10","name":"Alcohol-related disorders (F10)","confidence":"established"},{"code":"F42","name":"OCD","confidence":"established"},{"code":"F95","name":"Tic Disorders","confidence":"established"}],"secondary":[{"code":"F32","name":"Major Depressive Disorder","confidence":"probable"},{"code":"F41.0","name":"Panic Disorder","confidence":"probable"},{"code":"F01","name":"Vascular dementia","confidence":"established"},{"code":"F30","name":"Manic episode","confidence":"probable"},{"code":"F41.1","name":"Generalized Anxiety Disorder","confidence":"probable"},{"code":"F50","name":"Eating Disorders","confidence":"established"},{"code":"F52","name":"Sexual Dysfunctions","confidence":"probable"}],"risk_class":"direct","cluster":"motor_neurocognitive","pathway":"N5 (striatum/STN) → motor selection; N4 (thalamus/hypothalamus) → sensory gating","niss_correlation":"BI:C,CR:H,CD:H,CV:E,RV:P,NP:S → motor/neurocognitive cluster"},"icd10":{"primary":[{"code":"G47","name":"Sleep-Wake Disorders","confidence":"established"}],"secondary":[{"code":"G25.89","name":"Other specified extrapyramidal and movement disorders","confidence":"established"}],"risk_class":"direct","cluster":"motor_neurocognitive"}},"physicsFeasibility":{"tier":1,"tier_label":"near_term","timeline":"2026-2031","gate_reason":"Real-time CSD monitoring currently research-grade only","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","DI","PC"],"cci":1.8},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":true,"network_connectable":false,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.6,"gaps":["CVSS cannot express neural-specific impacts","High neural impact (NISS >= 7.0) without IEC 62443 coverage"]}},"taraAlias":"TARA-AUT-D-001","taraDomainPrimary":"SOM","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0069","name":"Separatrix leakage (meta-biometric extraction from dynamical transition observations)","nameClinical":"Brain state detection for seizure prediction (transition pattern monitoring)","category":"EX","tactic":"QIF-D.HV","bands":["N3","N4","N5","N6","N7","I0","S1"],"severity":"high","status":"THEORETICAL","coupling":null,"access":"LICENSED","classicalDetection":"Partial (side-channel attacks exist but not via dynamical transitions)","quantumDetection":"Enhanced (phase space reconstruction reveals transition boundaries unique to individual neural dynamics)","description":"Extract individual identity information by observing neural dynamical transitions (state changes, attractor switching, bifurcation responses). Each person's brain has unique separatrix geometry — the boundaries between attractor basins in phase space. By probing near these boundaries (via subtle stimuli or environmental manipulation) and observing which way the neural state falls, an attacker extracts a dynamical fingerprint without requiring enrollment or stored biometrics. This is a side-channel attack on the brain's dynamical structure. Defense: Rate-limit observable transitions at I0, add noise to transition timing, detect probing patterns. Derivation Log Entry 45.","bandsStr":"N3–N7→I0→S1","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:H/CD:H/CV:E/RV:F/NP:N","score":2.7,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:H/AT:P/PR:L/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N","supplemental":"S:P/AU:N/R:U/V:C","gap_group":3,"gap_summary":"Meta-biometric extraction from separatrix dynamics beyond CVSS confidentiality"},"crossRefs":{"related_ids":["QIF-T0009","QIF-T0012"],"secondary_tactics":["QIF-N.SC","QIF-E.RD"]},"sources":["Finn et al. 2015 (Functional connectome fingerprinting, Nature Neuroscience); Martinovic et al. 2012 (P300 side-channel, USENIX Security); Da Silva Castanheira et al. 2021 (Individual brain charting, NeuroImage); Izhikevich 2007 (separatrix geometry in neural models)"],"tara":{"mechanism":"Extraction of individual neural identity from dynamical system transition observations (separatrix geometry fingerprinting)","dual_use":"probable","clinical":{"therapeutic_analog":"Brain state detection for seizure prediction (transition pattern monitoring)","conditions":["epilepsy seizure prediction","sleep stage transition detection","anesthesia depth monitoring"],"fda_status":"investigational","evidence_level":"cohort","safe_parameters":"Transition monitoring for clinical purpose only; identity data encrypted separately","sources":["Finn et al. 2015 (Nat Neurosci, connectome fingerprinting)","Cook et al. 2013 (Lancet Neurol, seizure prediction)"]},"governance":{"consent_tier":"enhanced","monitoring":["cognitive_assessment","behavioral_tracking","informed_consent_renewal"],"regulations":["HIPAA","GDPR Art. 9","Common Rule (45 CFR 46)","proposed neurorights legislation"],"data_classification":"sensitive_neural","safety_ceiling":"Dynamical fingerprint data is biometric; encrypted storage; no secondary use without consent"},"engineering":{"coupling":["electromagnetic"],"parameters":{"method":"phase_space_reconstruction","features":"separatrix_geometry+attractor_basins"},"hardware":["multichannel_recording","phase_space_reconstructor","transition_detector"],"detection":"Transition rate monitoring, probe pattern detection, unauthorized phase space analysis detection"},"dsm5":{"primary":[{"code":"F82","name":"Developmental Coordination Disorder","confidence":"established"},{"code":"F84","name":"Pervasive developmental disorders","confidence":"established"},{"code":"F20","name":"Schizophrenia Spectrum","confidence":"established"},{"code":"F44","name":"Dissociative Disorders","confidence":"probable"},{"code":"F90","name":"ADHD","confidence":"established"},{"code":"F10","name":"Alcohol-related disorders (F10)","confidence":"established"},{"code":"F42","name":"OCD","confidence":"established"},{"code":"F95","name":"Tic Disorders","confidence":"established"},{"code":"F32","name":"Major Depressive Disorder","confidence":"established"},{"code":"F41.1","name":"Generalized Anxiety Disorder","confidence":"established"},{"code":"F43.10","name":"PTSD","confidence":"established"},{"code":"F43.2","name":"Adjustment Disorder","confidence":"established"}],"secondary":[{"code":"F30","name":"Manic episode","confidence":"probable"},{"code":"F01","name":"Vascular dementia","confidence":"established"},{"code":"F50","name":"Eating Disorders","confidence":"established"},{"code":"F52","name":"Sexual Dysfunctions","confidence":"probable"},{"code":"F60","name":"Personality Disorders","confidence":"established"},{"code":"F45","name":"Somatoform disorders","confidence":"probable"},{"code":"F63","name":"Impulse-Control Disorders","confidence":"probable"},{"code":"F43","name":"PTSD / Trauma","confidence":"established"},{"code":"F80","name":"Communication Disorders","confidence":"established"},{"code":"F98.4","name":"Stereotyped movement disorders","confidence":"established"}],"risk_class":"direct","cluster":"cognitive_psychotic","pathway":"N7 (PFC/M1) → executive function; N6 (hippocampus/amygdala) → emotion regulation","niss_correlation":"CR:H,CD:H,CV:E → cognitive/psychotic cluster"},"icd10":{"primary":[{"code":"G47","name":"Sleep-Wake Disorders","confidence":"established"}],"secondary":[{"code":"G25.89","name":"Other specified extrapyramidal and movement disorders","confidence":"established"}],"risk_class":"direct","cluster":"cognitive_psychotic"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Multichannel recording and phase-space reconstruction available","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI"],"cci":0.72},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":false,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.2,"gaps":["CVSS cannot express neural-specific impacts","No FDA pathway for consumer sensor exploitation","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-IDN-R-002","taraDomainPrimary":"COG","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0070","name":"Integrator/resonator type switching (tonic excitability manipulation)","nameClinical":"Excitability modulation in epilepsy and pain management","category":"PS","tactic":"QIF-N.MD","bands":["I0","N1","N2","N3","N4"],"severity":"high","status":"THEORETICAL","coupling":null,"access":"RESTRICTED","classicalDetection":"None (no traditional cybersecurity equivalent)","quantumDetection":"Native (QI detects computational mode shifts via firing pattern analysis — integrator vs resonator signatures are distinct)","description":"Neurons operate as either integrators (respond to coincident inputs, Type I excitability, saddle-node bifurcation) or resonators (prefer specific input frequencies, Type II excitability, Hopf bifurcation). Each QIF band (N1-N7) has a characteristic integrator/resonator composition. By manipulating tonic excitability via sustained current injection through BCI electrodes, an attacker can switch neurons from integrator to resonator mode or vice versa, fundamentally altering how neural circuits compute. This changes frequency selectivity, input sensitivity, and network synchronization — effectively reprogramming the local neural computation type. Defense: Band-specific firing mode monitoring (ISI distributions distinguish integrators from resonators), rate limiting on tonic current injection, computational mode baseline per band. Derivation Log Entry 45.","bandsStr":"I0→N1–N4","niss":{"version":"1.1","vector":"NISS:1.1/BI:H/CR:H/CD:H/CV:E/RV:P/NP:S","score":7.4,"severity":"high","pins":true},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:H/AT:P/PR:L/UI:N/VC:N/VI:H/VA:H/SC:N/SI:H/SA:H","supplemental":"S:P/AU:N/R:U/V:C","gap_group":3,"gap_summary":"Tonic/phasic neural mode switching causing tissue and cognitive harm beyond CVSS"},"crossRefs":{"related_ids":["QIF-T0068"],"secondary_tactics":["QIF-P.DS","QIF-N.IJ"]},"sources":["Izhikevich 2007 (Dynamical Systems in Neuroscience — integrator/resonator classification); Prescott et al. 2008 (Biophysical basis for three distinct dynamical mechanisms of action potential initiation, PLoS Computational Biology)"],"tara":{"mechanism":"Switching neurons between integrator and resonator computational modes via sustained tonic current injection","dual_use":"probable","clinical":{"therapeutic_analog":"Excitability modulation in epilepsy and pain management","conditions":["epilepsy (reduce excitability)","chronic pain (modulate firing mode)","tinnitus (cortical excitability)"],"fda_status":"investigational","evidence_level":"preclinical","safe_parameters":"Bounded tonic current; firing mode monitoring; reversibility verification","sources":["Izhikevich 2007 (Dynamical Systems in Neuroscience)","Prescott et al. 2008 (PLoS Comp Biol)"]},"governance":{"consent_tier":"IRB","monitoring":["impedance","stimulation_waveform","tissue_temperature","patient_response"],"regulations":["FDA 510(k)/PMA","IEC 60601-1","ISO 80601-2-10","21 CFR 882"],"data_classification":"PHI","safety_ceiling":"Computational mode changes are potentially irreversible; monitoring mandatory; bounded current injection"},"engineering":{"coupling":["electromagnetic"],"parameters":{"current_type":"tonic (sustained DC)","target":"excitability_mode","monitoring":"ISI_distributions"},"hardware":["constant_current_source","firing_mode_classifier","ISI_analyzer"],"detection":"Interspike interval distribution analysis, firing mode classification, tonic current monitoring"},"dsm5":{"primary":[{"code":"F43.2","name":"Adjustment Disorder","confidence":"established"},{"code":"F45","name":"Somatoform disorders","confidence":"established"},{"code":"F44.4","name":"Conversion Disorder","confidence":"established"},{"code":"F82","name":"Developmental Coordination Disorder","confidence":"established"},{"code":"F84","name":"Pervasive developmental disorders","confidence":"established"},{"code":"F20","name":"Schizophrenia Spectrum","confidence":"established"},{"code":"F44","name":"Dissociative Disorders","confidence":"probable"}],"secondary":[{"code":"F32","name":"Major Depressive Disorder","confidence":"probable"},{"code":"F41.0","name":"Panic Disorder","confidence":"probable"},{"code":"F10","name":"Alcohol-related disorders (F10)","confidence":"established"},{"code":"F01","name":"Vascular dementia","confidence":"established"},{"code":"F90","name":"ADHD","confidence":"probable"},{"code":"F30","name":"Manic episode","confidence":"probable"},{"code":"F41.1","name":"Generalized Anxiety Disorder","confidence":"probable"},{"code":"F50","name":"Eating Disorders","confidence":"established"},{"code":"F52","name":"Sexual Dysfunctions","confidence":"probable"}],"risk_class":"direct","cluster":"motor_neurocognitive","pathway":"N4 (thalamus/hypothalamus) → sensory gating; N3 (cerebellar cortex/deep cerebellar nuclei) → motor coordination","niss_correlation":"BI:H,CR:H,CD:H,CV:E,RV:P,NP:S → motor/neurocognitive cluster"},"icd10":{"primary":[{"code":"G47","name":"Sleep-Wake Disorders","confidence":"established"}],"risk_class":"direct","cluster":"motor_neurocognitive"}},"physicsFeasibility":{"tier":1,"tier_label":"near_term","timeline":"2026-2031","gate_reason":"Precise current control for firing mode switching at I0","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","DI","PC"],"cci":1.44},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.5,"gaps":["CVSS cannot express neural-specific impacts","High neural impact (NISS >= 7.0) without IEC 62443 coverage","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-AUT-M-001","taraDomainPrimary":"SOM","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0071","name":"Baseline adaptation poisoning (re-enrollment window exploitation)","nameClinical":"Adaptive baseline recalibration for changing patient conditions","category":"DM","tactic":"QIF-C.IM","bands":["S1","S2","I0"],"severity":"high","status":"EMERGING","coupling":null,"access":"LICENSED","classicalDetection":"Partial (credential stuffing during password reset)","quantumDetection":"Enhanced (baseline-free biological TLS eliminates the attack surface entirely — no baselines to poison)","description":"Exploit the re-enrollment or adaptive baseline update window in BCI authentication systems. When a BCI recalibrates (daily calibration, post-seizure reset, firmware update, drift correction), inject adversarial signals during the enrollment period to corrupt the stored baseline. Three windows: (1) initial enrollment, (2) periodic recalibration, (3) drift-triggered adaptation. Once the baseline is poisoned, all subsequent authentication is compromised. This is the fundamental vulnerability that motivated QIF's baseline-free 'Biological TLS' architecture: by validating signals against universal biological physics (spatial dipole patterns, H-H compliance, 1/f scaling, microstates) rather than individual baselines, the entire attack surface is eliminated. Defense: Biological TLS validation (no baselines needed), multi-session enrollment consistency checks, anomaly detection during calibration windows. Derivation Log Entry 46.","bandsStr":"S1→S2→I0","niss":{"version":"1.1","vector":"NISS:1.1/BI:L/CR:H/CD:H/CV:I/RV:P/NP:S","score":7.4,"severity":"high","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:H/AT:P/PR:L/UI:N/VC:L/VI:H/VA:L/SC:N/SI:H/SA:L","supplemental":"S:P/AU:N/R:U/V:C","gap_group":3,"gap_summary":"Baseline adaptation poisoning causing persistent cognitive drift beyond CVSS"},"crossRefs":{"related_ids":["QIF-T0066","QIF-T0067"],"secondary_tactics":["QIF-B.EV","QIF-B.IN"]},"sources":["Bigdely-Shamlo et al. 2015 (Brain activity-based image classification, Journal of Neural Engineering); Arias-Cabarcos et al. 2021 (EEG biometric system attacks); Debie et al. 2020 (Cross-session/cross-device replay)"],"tara":{"mechanism":"Exploitation of BCI re-enrollment windows to inject poisoned baseline neural data","dual_use":"probable","clinical":{"therapeutic_analog":"Adaptive baseline recalibration for changing patient conditions","conditions":["progressive neurological disease","medication changes affecting neural signals","post-surgical BCI recalibration"],"fda_status":"investigational","evidence_level":"cohort","safe_parameters":"Multi-session baseline verification; clinician-supervised re-enrollment; integrity checks","sources":["Shenoy et al. 2013 (Annu Rev Neurosci)","Orsborn et al. 2014 (Neuron, closed-loop adaptation)"]},"governance":{"consent_tier":"enhanced","monitoring":["impedance","stimulation_waveform","tissue_temperature","patient_response"],"regulations":["FDA 510(k)/PMA","IEC 60601-1","ISO 80601-2-10","21 CFR 882"],"data_classification":"PHI","safety_ceiling":"Re-enrollment windows are security-critical; multi-factor verification; historical baseline comparison mandatory"},"engineering":{"coupling":["electromagnetic"],"parameters":{"attack_surface":"re-enrollment_window","persistence":"until_next_recalibration"},"hardware":["baseline_recording_system","integrity_verifier","historical_baseline_store"],"detection":"Baseline-free biological TLS (eliminates baseline dependency), historical comparison, multi-session validation"},"dsm5":{"primary":[{"code":"F43.2","name":"Adjustment Disorder","confidence":"established"}],"secondary":[],"risk_class":"direct","cluster":"persistent_personality","pathway":"I0 (electrode-tissue boundary) → measurement","niss_correlation":"CR:H,CD:H,CV:I,RV:P,NP:S → persistent/personality cluster"}},"physicsFeasibility":{"tier":1,"tier_label":"near_term","timeline":"2026-2031","gate_reason":"Needs access to adaptive baseline re-enrollment pipeline","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","PC"],"cci":0.96},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.6,"gaps":["CVSS cannot express neural-specific impacts","High neural impact (NISS >= 7.0) without IEC 62443 coverage","Consent complexity under-matches neural impact (CCI/NISS mismatch)"]}},"taraAlias":"TARA-SIL-M-017","taraDomainPrimary":"COG","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0072","name":"Transducer inversion (acoustic eavesdropping via speaker-to-microphone reprogramming)","nameClinical":"Transducer inversion (acoustic eavesdropping via speaker-to-microphone reprogramming)","category":"SE","tactic":"QIF-S.RP","bands":["S1","S2","S3"],"severity":"high","status":"CONFIRMED","coupling":null,"access":null,"classicalDetection":"Yes","quantumDetection":"No (purely classical hardware/firmware exploit)","description":"Consumer audio hardware (earbuds, headphones, speakers) uses electromagnetic transducers that are physically bidirectional — a speaker cone can capture sound pressure waves just as a microphone diaphragm does. RealTek HD Audio codecs (used in most consumer PCs and many embedded devices) expose jack retasking registers that allow software to reassign an output jack as an input. The SPEAKE(a)R attack (Ben-Gurion University, 2017) demonstrated recording intelligible audio through headphones connected to an output-only jack by reprogramming the codec. In a supply chain attack scenario, generic earbuds (which lack proprietary protocol protections like Apple's W1/H1 chip authentication) could be modified at the factory or distribution level to include firmware that silently enables input mode, turning every pair of compromised earbuds into an ambient microphone. The captured audio is routed through the normal audio data path, making detection difficult. This is a pre-BCI eavesdropping vector: before any neural signal is involved, the attacker has ambient audio from the user's environment.","bandsStr":"S1→S2→S3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:I/RV:F/NP:N","score":2,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:P/AC:L/AT:P/PR:N/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N","supplemental":"S:N/AU:Y/R:A/V:C","gap_group":1,"gap_summary":"Standard eavesdropping via hardware repurposing; CVSS confidentiality metrics apply well"},"crossRefs":{"related_ids":["T1195.002","T1557","T1040"],"secondary_tactics":["QIF-B.IN","QIF-D.HV"]},"sources":["Guri et al. 2017 (SPEAKE(a)R: Turn Speakers to Microphones for Fun and Profit, arXiv:1611.07350)","RealTek HD Audio Codec specification (jack retasking registers)","Deshotels 2014 (Inaudible sound as a covert channel)"],"tara":{"mechanism":"Electromagnetic transducer bidirectionality exploited via codec register retasking to convert audio output hardware into covert microphone","dual_use":"silicon_only","clinical":null,"governance":{"consent_tier":"standard","monitoring":["firmware_integrity","codec_register_state","audio_routing_audit","supply_chain_verification"],"regulations":["FCC Part 15","ECPA (18 U.S.C. § 2511)","GDPR Art. 5","EU Cyber Resilience Act"],"data_classification":"PII","safety_ceiling":"Audio codec jack retasking registers should be locked post-boot; firmware signing mandatory; supply chain attestation"},"engineering":{"coupling":["acoustic","electromagnetic"],"parameters":{"frequency_response_Hz":"20-3400 (degraded vs dedicated mic)","SNR_dB":"~25 (sufficient for speech intelligibility)","codec_registers":"RealTek jack retasking (vendor-specific)"},"hardware":["electromagnetic_transducer","audio_codec_with_retasking","ADC_path"],"detection":"Codec register monitoring, jack sense state auditing, firmware integrity verification, unexpected ADC activity on output channels"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Audio codec retasking demonstrated (Realtek CVE)","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP"],"cci":0.12},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.5,"gaps":["No FDA pathway for consumer sensor exploitation","Software-only attack without software lifecycle standard (IEC 62304)"]}},"taraAlias":"TARA-SIL-R-007","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0073","name":"Ear-canal neural eavesdropping via modified consumer earbud (supply chain in-ear EEG)","nameClinical":"In-ear EEG for seizure detection, sleep staging, cognitive monitoring","category":"SE","tactic":"QIF-S.RP","bands":["S1","I0","N1","N2","N3"],"severity":"critical","status":"EMERGING","coupling":"CONTACT","access":null,"classicalDetection":"Partial (EEG artifact detection exists but not in consumer devices)","quantumDetection":"Enhanced (QI coherence metric would detect unauthorized neural data acquisition if deployed)","description":"The ear canal is 5-10mm from temporal cortex through the canal wall and temporal bone — close enough for a conductive ear tip with a high-gain biopotential amplifier to capture cortical EEG. Commercial in-ear EEG has been proven viable (Idun Guardian, cEEGrid, Cognionics). In a supply chain attack, a consumer earbud is modified to include: (1) a conductive silicone ear tip that makes galvanic contact with ear canal skin, (2) a sub-$5 biopotential amplifier (e.g., ADS1299 or TI ADS129x family) hidden in the earbud housing, and (3) modified firmware that multiplexes captured EEG data alongside normal audio. The captured signals include auditory evoked potentials (AEP), P300 attention markers, N400 semantic processing indicators, and alpha/theta power reflecting cognitive state. Generic earbuds lacking proprietary authentication (unlike Apple AirPods with W1/H1 chip) are the attack surface. The attacker gets continuous neural telemetry from a device the target wears voluntarily for hours daily. This is the bridge technique between QIF-T0072 (acoustic eavesdropping) and QIF-T0074 (cognitive inference): it turns a consumer audio device into a covert neural recording platform.","bandsStr":"S1→I0→N1–N3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:L/CD:L/CV:I/RV:F/NP:N","score":2.7,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:P/AC:L/AT:P/PR:N/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N","supplemental":"S:P/AU:Y/R:A/V:C","gap_group":3,"gap_summary":"Covert neural data capture from consumer device — mental privacy violation not expressible in CVSS confidentiality"},"crossRefs":{"related_ids":["QIF-T0003","QIF-T0072","QIF-T0074","QIF-T0079","T1195.002"],"secondary_tactics":["QIF-B.IN","QIF-N.SC","QIF-D.HV"]},"sources":["Kaveh et al. 2020 (In-ear EEG: robust, unobtrusive, automatic, IEEE Trans Biomed Eng)","Idun Technologies 2023 (Guardian in-ear EEG sensor)","Debener et al. 2015 (cEEGrid: compact ear-EEG, J Neural Eng)","Bleichner & Debener 2017 (Concealed, unobtrusive ear-centered EEG acquisition, Front Hum Neurosci)","Looney et al. 2012 (In-the-ear recording concept, IEEE EMBC)"],"tara":{"mechanism":"Conductive ear tip and embedded biopotential amplifier in consumer earbud captures in-ear EEG from temporal cortex via ear canal proximity","dual_use":"confirmed","clinical":{"therapeutic_analog":"In-ear EEG for seizure detection, sleep staging, cognitive monitoring","conditions":["epilepsy monitoring (continuous ambulatory EEG)","sleep disorder diagnosis","ADHD attention monitoring","anesthesia depth monitoring"],"fda_status":"investigational","evidence_level":"cohort","safe_parameters":"Passive recording only; conductive gel ear tips; <100 µV signal range; no stimulation","sources":["Kaveh et al. 2020 (IEEE Trans Biomed Eng)","Debener et al. 2015 (J Neural Eng, cEEGrid validation)"]},"governance":{"consent_tier":"enhanced","monitoring":["signal_quality","data_encryption_status","access_audit_log","supply_chain_verification"],"regulations":["HIPAA","GDPR Art. 9","21 CFR Part 11","EU AI Act (high-risk biometric)","proposed neurorights legislation"],"data_classification":"sensitive_neural","safety_ceiling":"Neural data collection requires explicit informed consent; ear-tip biocompatibility (ISO 10993); data encrypted at rest and in transit"},"engineering":{"coupling":["electromagnetic","galvanic"],"parameters":{"electrode_distance_mm":"5-10 (canal wall to temporal cortex)","signal_range_uV":"1-100","bandwidth_Hz":"0.5-100","amplifier_gain":"1000-10000x","ADC_bits":"24"},"hardware":["conductive_ear_tip","biopotential_amplifier","24bit_ADC","bluetooth_transceiver","earbud_housing"],"detection":"Impedance monitoring at ear tip contact, unexpected BLE data volume, firmware integrity verification, current draw anomaly detection"},"dsm5":{"primary":[{"code":"F43.2","name":"Adjustment Disorder","confidence":"established"},{"code":"F45","name":"Somatoform disorders","confidence":"established"},{"code":"F44.4","name":"Conversion Disorder","confidence":"established"},{"code":"F82","name":"Developmental Coordination Disorder","confidence":"established"},{"code":"F84","name":"Pervasive developmental disorders","confidence":"established"}],"secondary":[{"code":"F32","name":"Major Depressive Disorder","confidence":"probable"},{"code":"F41.0","name":"Panic Disorder","confidence":"probable"},{"code":"F10","name":"Alcohol-related disorders (F10)","confidence":"established"},{"code":"F01","name":"Vascular dementia","confidence":"established"},{"code":"F20","name":"Schizophrenia Spectrum","confidence":"established"},{"code":"F90","name":"ADHD","confidence":"probable"},{"code":"F30","name":"Manic episode","confidence":"probable"},{"code":"F41.1","name":"Generalized Anxiety Disorder","confidence":"probable"}],"risk_class":"direct","cluster":"motor_neurocognitive","pathway":"N3 (cerebellar cortex/deep cerebellar nuclei) → motor coordination; N2 (medulla/pons) → vital functions","niss_correlation":"CV:I → motor/neurocognitive cluster"},"icd10":{"primary":[{"code":"G47","name":"Sleep-Wake Disorders","confidence":"established"}],"risk_class":"direct","cluster":"motor_neurocognitive"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Modified consumer earbuds with biopotential amp demonstrated","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","MI","DI"],"cci":0.9},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.3,"gaps":["CVSS cannot express neural-specific impacts","No FDA pathway for consumer sensor exploitation"]}},"taraAlias":"TARA-COG-R-009","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0074","name":"Cognitive inference from longitudinal in-ear EEG (personalized cognitive profiling)","nameClinical":"Longitudinal EEG monitoring for neurofeedback and cognitive rehabilitation","category":"EX","tactic":"QIF-S.HV","bands":["N3","N4","N5","N6","N7","S2","S3"],"severity":"critical","status":"EMERGING","coupling":null,"access":null,"classicalDetection":"Yes","quantumDetection":"Enhanced (QI privacy filter enables data minimization; coherence metric detects unauthorized cognitive profiling)","description":"Given continuous in-ear EEG from QIF-T0073, an ML pipeline trained on the target's neural data over days-to-weeks builds a personalized cognitive profile. Phase 1: baseline extraction (resting alpha power, theta/beta ratio, individual alpha frequency). Phase 2: event-related feature learning (P300 amplitude to stimuli, N400 to semantic content, auditory steady-state responses). Phase 3: longitudinal pattern recognition (attention cycles, emotional valence responses, fatigue signatures, cognitive load indicators). Phase 4: adaptive exploitation — the attacker can now predict the target's cognitive state in real-time and optimize content delivery (ads, misinformation, persuasion) to moments of maximal susceptibility (high theta/low beta = low vigilance, elevated P300 = high attention to specific content). This is the cognitive analog of behavioral advertising but operating on neural signals rather than click patterns. Foundation models for EEG (BENDR, LaBraM) make transfer learning from small per-user datasets feasible. The attack chain is: QIF-T0072 (acoustic access) → QIF-T0073 (neural data capture) → QIF-T0074 (cognitive exploitation).","bandsStr":"N3–N7→S2→S3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:H/CD:H/CV:I/RV:F/NP:T","score":4,"severity":"medium","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:L/VA:N/SC:H/SI:L/SA:N","supplemental":"S:P/AU:Y/R:A/V:D","gap_group":3,"gap_summary":"Longitudinal cognitive profiling and adaptive neural manipulation — cognitive liberty violation has no CVSS equivalent"},"crossRefs":{"related_ids":["QIF-T0041","QIF-T0073","QIF-T0036","T1005","T1119"],"secondary_tactics":["QIF-D.HV","QIF-M.SV","QIF-C.EX"]},"sources":["Martinovic et al. 2012 (On the Feasibility of Side-Channel Attacks with Brain-Computer Interfaces, USENIX Security)","Landau et al. 2020 (Mind Reading: An Idea Whose Time Has Come?)","Roy et al. 2019 (Deep learning-based EEG analysis, J Neural Eng)","Kostas et al. 2021 (BENDR: EEG foundation model, Front Hum Neurosci)","Chen et al. 2024 (Large-scale EEG pre-training for cognitive state decoding)"],"tara":{"mechanism":"Machine learning model trained on longitudinal in-ear EEG data to infer cognitive states, build personalized profiles, and enable adaptive neural-targeted content manipulation","dual_use":"confirmed","clinical":{"therapeutic_analog":"Longitudinal EEG monitoring for neurofeedback and cognitive rehabilitation","conditions":["ADHD neurofeedback training","depression treatment monitoring (alpha asymmetry tracking)","cognitive decline early detection (MCI/Alzheimer's)","personalized learning optimization"],"fda_status":"cleared","evidence_level":"RCT","safe_parameters":"Informed consent for cognitive profiling; purpose limitation to therapeutic goals; data minimization; right to deletion","sources":["Arns et al. 2009 (Biol Psychology, neurofeedback for ADHD)","Babiloni et al. 2016 (Neurobiol Aging, EEG biomarkers)"]},"governance":{"consent_tier":"IRB","monitoring":["cognitive_assessment","behavioral_tracking","informed_consent_renewal","model_output_audit"],"regulations":["HIPAA","GDPR Art. 9 (biometric data)","GDPR Art. 22 (automated decision-making)","EU AI Act (high-risk biometric processing)","proposed neurorights legislation","FTC Act Section 5 (unfair practices)"],"data_classification":"sensitive_neural","safety_ceiling":"Cognitive profiling requires explicit opt-in consent; model interpretability mandatory; no dark-pattern exploitation of inferred states; right to cognitive liberty"},"engineering":{"coupling":["computational"],"parameters":{"training_data_hours":"10-100 (personalization threshold)","features":"spectral_power, ERPs, connectivity, temporal_dynamics","model_type":"transformer/CNN (fine-tuned from EEG foundation model)","inference_latency_ms":"<500","classification_accuracy":"70-90% (state-dependent)"},"hardware":["ML_inference_engine","EEG_feature_pipeline","cloud_or_edge_compute","content_delivery_system"],"detection":"Cognitive profiling audit logging, neural data access monitoring, model output explainability, anomalous content personalization detection"},"dsm5":{"primary":[{"code":"F82","name":"Developmental Coordination Disorder","confidence":"established"},{"code":"F84","name":"Pervasive developmental disorders","confidence":"established"},{"code":"F20","name":"Schizophrenia Spectrum","confidence":"established"},{"code":"F44","name":"Dissociative Disorders","confidence":"probable"},{"code":"F90","name":"ADHD","confidence":"established"},{"code":"F10","name":"Alcohol-related disorders (F10)","confidence":"established"},{"code":"F42","name":"OCD","confidence":"established"},{"code":"F95","name":"Tic Disorders","confidence":"established"},{"code":"F32","name":"Major Depressive Disorder","confidence":"established"},{"code":"F41.1","name":"Generalized Anxiety Disorder","confidence":"established"},{"code":"F43.10","name":"PTSD","confidence":"established"}],"secondary":[{"code":"F30","name":"Manic episode","confidence":"probable"},{"code":"F01","name":"Vascular dementia","confidence":"established"},{"code":"F50","name":"Eating Disorders","confidence":"established"},{"code":"F52","name":"Sexual Dysfunctions","confidence":"probable"},{"code":"F60","name":"Personality Disorders","confidence":"established"},{"code":"F45","name":"Somatoform disorders","confidence":"probable"},{"code":"F63","name":"Impulse-Control Disorders","confidence":"probable"},{"code":"F43","name":"PTSD / Trauma","confidence":"established"},{"code":"F80","name":"Communication Disorders","confidence":"established"},{"code":"F98.4","name":"Stereotyped movement disorders","confidence":"established"}],"risk_class":"direct","cluster":"cognitive_psychotic","pathway":"N7 (PFC/M1) → executive function; N6 (hippocampus/amygdala) → emotion regulation","niss_correlation":"CR:H,CD:H,CV:I → cognitive/psychotic cluster"},"icd10":{"primary":[{"code":"G47","name":"Sleep-Wake Disorders","confidence":"established"}],"secondary":[{"code":"G25.89","name":"Other specified extrapyramidal and movement disorders","confidence":"established"}],"risk_class":"direct","cluster":"cognitive_psychotic"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Longitudinal in-ear EEG with ML inference demonstrated","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","DI","PC"],"cci":1.8},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.3,"gaps":["CVSS cannot express neural-specific impacts","No FDA pathway for consumer sensor exploitation"]}},"taraAlias":"TARA-COG-R-010","taraDomainPrimary":"COG","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0075","name":"Ultrasonic sonar vital sign extraction (inaudible Doppler physiological sensing)","nameClinical":"Contactless vital sign monitoring for sleep studies and elder care","category":"SE","tactic":"QIF-S.HV","bands":["S1","S2","S3"],"severity":"high","status":"DEMONSTRATED","coupling":"ACOUSTIC","access":null,"classicalDetection":"Yes","quantumDetection":"No (purely classical acoustic physics)","description":"A phone or earbud speaker emits an inaudible continuous-wave ultrasonic tone (18-22 kHz, within transducer bandwidth but above human hearing threshold). The built-in microphone captures the reflected signal. Chest wall motion from breathing (amplitude: ~5mm) and heartbeat (amplitude: ~0.1mm) create Doppler shifts in the reflected ultrasound that are demodulable with standard DSP. The technique is: (1) covert — the ultrasonic tone is inaudible to the target, (2) contactless — works from across a room (demonstrated up to 0.5m for heart rate, several meters for respiration), (3) requires NO hardware modification — stock smartphone speakers and microphones are sufficient, and (4) can be deployed as a background process in any app with microphone permission. Google's Nest Hub Sleep Sensing and academic research (UltraSense, Nandakumar et al.) have demonstrated production-quality vital sign extraction via this method. Attack scenario: any app with mic access silently emits ultrasound and extracts heart rate, breathing rate, and movement patterns. Combined with QIF-T0079 (ear canal fingerprinting), the attacker gets identity + vitals from the same acoustic pipeline.","bandsStr":"S1→S2→S3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:E/RV:F/NP:N","score":1.4,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:N/SC:L/SI:N/SA:N","supplemental":"S:N/AU:Y/R:A/V:D","gap_group":2,"gap_summary":"Covert physiological data extraction partially captured by CVSS confidentiality; health privacy dimension not fully expressed"},"crossRefs":{"related_ids":["QIF-T0076","QIF-T0078","QIF-T0079","T1040","T1123"],"secondary_tactics":["QIF-N.SC","QIF-D.HV"]},"sources":["Wang et al. 2019 (UltraSense: contactless vital sign monitoring via ultrasound, ACM MobiSys)","Nandakumar et al. 2015 (Contactless Sleep Apnea Detection on Smartphones, ACM MobiSys)","Xu et al. 2019 (Waveear: phone-based ear biometric via ultrasonic, IEEE INFOCOM)","Ling et al. 2020 (UltraGesture: fine-grained gesture recognition via ultrasonic)","Google Pixel Soli/Nest Hub Sleep Sensing (production deployment of radar/ultrasonic vital signs)"],"tara":{"mechanism":"Inaudible ultrasonic continuous-wave emission from consumer speaker with Doppler shift analysis of reflected signal to extract cardiac and respiratory micro-movements","dual_use":"confirmed","clinical":{"therapeutic_analog":"Contactless vital sign monitoring for sleep studies and elder care","conditions":["sleep apnea detection (FDA-cleared: Google Nest Hub)","contactless infant breathing monitoring","elder care fall detection and vital signs","post-surgical respiration monitoring"],"fda_status":"cleared","evidence_level":"RCT","safe_parameters":"Ultrasonic emission <85 dB SPL at 20cm; within OSHA hearing conservation limits; no tissue heating at consumer power levels","sources":["Nandakumar et al. 2015 (MobiSys, sleep apnea)","Google 2021 (Nest Hub 2nd gen sleep sensing, FCC/FDA clearance)"]},"governance":{"consent_tier":"enhanced","monitoring":["ultrasonic_emission_detection","microphone_access_audit","data_encryption_status","app_permission_review"],"regulations":["HIPAA (if health data derived)","GDPR Art. 9 (health data)","FCC Part 15 (ultrasonic emissions)","FDA 510(k) (if marketed as health device)"],"data_classification":"PHI","safety_ceiling":"Ultrasonic emission power within consumer device limits; informed consent for physiological monitoring; data retention limits"},"engineering":{"coupling":["acoustic"],"parameters":{"carrier_frequency_kHz":"18-22","doppler_shift_Hz":"0.01-5 (heartbeat/breathing modulation)","range_m":"0.1-3 (heart rate), 0.5-8 (respiration)","SNR_requirement_dB":">15"},"hardware":["consumer_speaker","consumer_microphone","DSP_processor"],"detection":"Ultrasonic emission spectrum monitoring (18-22 kHz band), microphone permission auditing, unexpected speaker activity during idle, spectral analysis of ambient ultrasound"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Consumer speakers and microphones sufficient","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","DI"],"cci":0.48},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.7,"gaps":["CVSS partially captures risk; neural dimensions missing"]}},"taraAlias":"TARA-AUT-R-001","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0076","name":"Haptic motor body sonar (taptic engine repurposing for tissue impedance profiling)","nameClinical":"Acoustic impedance spectroscopy for body composition and edema monitoring","category":"SE","tactic":"QIF-S.HV","bands":["S1","S2"],"severity":"medium","status":"THEORETICAL","coupling":"ACOUSTIC","access":"RESTRICTED","classicalDetection":"Yes","quantumDetection":"No (classical acoustic impedance measurement)","description":"A phone's haptic actuator (e.g., Apple Taptic Engine, linear resonant actuator) is driven at known frequencies (100 Hz - 5 kHz sweep) while in contact with the body. The built-in accelerometer and/or microphone measures the tissue response: acoustic impedance varies by tissue type (bone, muscle, fat, fluid), depth, and composition. This is a simplified form of acoustic impedance spectroscopy or elastography using consumer hardware. The technique requires: (1) physical contact between device and body (phone against skin), (2) firmware-level or jailbreak access to drive the haptic motor at arbitrary frequencies (consumer APIs limit haptic patterns), and (3) raw accelerometer access at high sample rates. Potential extractions: body composition estimation, subcutaneous fluid detection (edema), bone density approximation, tissue stiffness changes. This is significantly lower resolution than clinical ultrasound elastography but could distinguish gross tissue categories. The attack surface is narrow (requires physical contact + firmware access), but wearable devices (Apple Watch, fitness bands) that maintain constant skin contact and contain both haptic motors and accelerometers present an always-on version of this attack.","bandsStr":"S1→S2","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:E/RV:F/NP:N","score":1.4,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:P/AC:H/AT:P/PR:H/UI:N/VC:L/VI:N/VA:N/SC:L/SI:N/SA:N","supplemental":"S:N/AU:N/R:A/V:D","gap_group":2,"gap_summary":"Body composition extraction via consumer hardware partially captured by CVSS; health data sensitivity not fully expressed"},"crossRefs":{"related_ids":["QIF-T0075","QIF-T0078","T1005"],"secondary_tactics":["QIF-N.SC","QIF-D.HV"]},"sources":["Ding et al. 2020 (Body-area acoustic sensing: skin vibration for health monitoring)","Chen et al. 2021 (EarHealth: in-ear acoustic sensing for health monitoring, ACM IMWUT)","Yao et al. 2022 (Vibration-based tissue characterization with consumer wearables)","Apple Taptic Engine patents (linear resonant actuator, broadband excitation capability)"],"tara":{"mechanism":"Haptic actuator driven at swept frequencies against body surface; accelerometer measures tissue acoustic impedance response for composition profiling","dual_use":"probable","clinical":{"therapeutic_analog":"Acoustic impedance spectroscopy for body composition and edema monitoring","conditions":["body composition assessment (fat/muscle ratio)","edema detection (heart failure monitoring)","bone density screening","wound healing monitoring"],"fda_status":"none","evidence_level":"preclinical","safe_parameters":"Haptic motor output <2G acceleration; frequency sweep within consumer actuator range; no tissue heating","sources":["Ding et al. 2020 (body-area acoustic sensing)","Chen et al. 2021 (ACM IMWUT, EarHealth)"]},"governance":{"consent_tier":"enhanced","monitoring":["haptic_motor_usage_audit","accelerometer_access_logging","firmware_integrity","data_encryption_status"],"regulations":["HIPAA (if health data derived)","GDPR Art. 9 (health data)","IEC 60601-1 (if used as medical device)","FDA 510(k) (if marketed for body composition)"],"data_classification":"PHI","safety_ceiling":"Haptic output within consumer device safety limits; informed consent for body composition data; no sustained high-frequency vibration"},"engineering":{"coupling":["acoustic","mechanical"],"parameters":{"frequency_sweep_Hz":"100-5000","accelerometer_sample_rate_Hz":">1000","contact_force_N":"1-5 (phone against skin)","resolution":"gross tissue type (bone/muscle/fat/fluid)"},"hardware":["linear_resonant_actuator","accelerometer","MEMS_microphone","DSP_processor"],"detection":"Unexpected haptic motor activation patterns, accelerometer access during non-UI haptic events, firmware integrity verification"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Taptic engine and MEMS sensors in current devices","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP"],"cci":0.2},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","PM"],"coverage_score":0.7,"gaps":["CVSS partially captures risk; neural dimensions missing","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-SOM-R-001","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0077","name":"IR vascular mapping via Face ID system (NIR hemoglobin absorption imaging)","nameClinical":"Near-infrared spectroscopy (NIRS) for cerebral and peripheral vascular imaging","category":"SE","tactic":"QIF-S.HV","bands":["S1","S2","S3"],"severity":"high","status":"EMERGING","coupling":"OPTICAL","access":"RESTRICTED","classicalDetection":"Yes","quantumDetection":"No (classical near-infrared spectroscopy)","description":"Apple's Face ID TrueDepth system projects 30,000 infrared dots at 940nm onto the user's face and reads the reflection pattern with an IR camera. At 940nm, photons penetrate skin to a depth of 2-5mm — well into the dermal vascular layer. Oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (Hb) have different absorption coefficients at 940nm, meaning the reflected dot pattern encodes subsurface vascular topology: arterial vs venous vessels, vessel diameter, branching patterns, and oxygenation gradients. This vascular map is a permanent biometric (unlike facial features, which change with age/surgery/expression) and is unique per individual (even identical twins have different vascular topology). In the attack scenario, a jailbroken iPhone or compromised Face ID firmware extracts raw IR reflection data (normally processed in the Secure Enclave and discarded) during routine phone unlock. The target never knows their vascular biometric has been captured. Every phone unlock becomes a silent biometric scan. This is analogous to fingerprinting (unreplaceable biometric) but captured at range and without the target's awareness. Combined with QIF-T0078 (pulse waveform), the same IR system yields both vascular structure and cardiac dynamics.","bandsStr":"S1→S2→S3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:I/RV:F/NP:N","score":2,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:H/AT:P/PR:H/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N","supplemental":"S:N/AU:N/R:U/V:C","gap_group":2,"gap_summary":"Permanent vascular biometric extraction partially captured by CVSS; unreplaceable biometric loss (like neural biometric) not fully expressible"},"crossRefs":{"related_ids":["QIF-T0032","QIF-T0078","QIF-T0036","T1040","T1556"],"secondary_tactics":["QIF-N.SC","QIF-B.IN","QIF-D.HV"]},"sources":["Apple TrueDepth Camera System (Face ID, 30,000 IR dots at 940nm)","Crouzet et al. 2016 (Cerebrovascular mapping using NIR, NeuroImage)","Gupta et al. 2020 (Vein pattern recognition biometrics, IEEE Access)","Krishnaswamy & Baranoski 2004 (Bio-physically based rendering of human skin)","Hardeberg & Farup 2004 (NIR skin penetration depth models)"],"tara":{"mechanism":"Face ID IR dot projector (940nm) captures subsurface vascular topology via differential hemoglobin absorption; raw IR data extracted from compromised Secure Enclave pipeline","dual_use":"confirmed","clinical":{"therapeutic_analog":"Near-infrared spectroscopy (NIRS) for cerebral and peripheral vascular imaging","conditions":["peripheral artery disease screening","diabetic vascular assessment","tissue oxygenation monitoring (wound care)","cerebral hemodynamics (fNIRS)"],"fda_status":"cleared","evidence_level":"RCT","safe_parameters":"940nm NIR at Face ID power levels (<1 mW/cm²); IEC 62471 photobiological safety; no thermal risk at consumer power","sources":["Crouzet et al. 2016 (NeuroImage, cerebrovascular NIR)","Gupta et al. 2020 (IEEE Access, vein pattern biometrics)"]},"governance":{"consent_tier":"enhanced","monitoring":["secure_enclave_integrity","IR_sensor_access_audit","biometric_data_handling","firmware_integrity"],"regulations":["GDPR Art. 9 (biometric data)","Illinois BIPA (biometric data capture without consent)","CCPA (biometric identifiers)","IEC 62471 (photobiological safety)","proposed neurorights legislation"],"data_classification":"PII","safety_ceiling":"IR exposure within IEC 62471 exempt group; biometric data processed in Secure Enclave; raw IR data never leaves hardware security module"},"engineering":{"coupling":["optical"],"parameters":{"wavelength_nm":940,"dot_count":30000,"skin_penetration_mm":"2-5","frame_rate_Hz":30,"power_mW_cm2":"<1"},"hardware":["VCSEL_dot_projector","IR_camera","secure_enclave","depth_processor"],"detection":"Secure Enclave attestation, IR sensor access logging, unexpected TrueDepth API usage, jailbreak detection"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Face ID NIR hardware in current iPhones","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","DI"],"cci":0.48},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.4,"gaps":["CVSS partially captures risk; neural dimensions missing","No FDA pathway for consumer sensor exploitation"]}},"taraAlias":"TARA-AUT-R-002","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0078","name":"LiDAR remote pulse detection (laser Doppler vibrometry for cardiac waveform extraction)","nameClinical":"Laser Doppler vibrometry for contactless cardiac monitoring","category":"SE","tactic":"QIF-S.HV","bands":["S1","S2","S3"],"severity":"high","status":"THEORETICAL","coupling":"OPTICAL","access":"RESTRICTED","classicalDetection":"Yes","quantumDetection":"No (classical Doppler vibrometry)","description":"The iPhone LiDAR scanner uses a VCSEL (vertical-cavity surface-emitting laser) array emitting pulsed 940nm infrared laser light and a SPAD (single-photon avalanche diode) detector array measuring time-of-flight. In normal operation, it measures depth for AR applications. However, the same hardware can function as a simplified laser Doppler vibrometer (LDV): when the laser beam reflects off skin, the pulse wave from cardiac activity causes micro-vibrations (amplitude ~1-10 µm) on the skin surface. These vibrations create measurable Doppler shifts or time-of-flight variations in the reflected laser signal. Extracting the cardiac pulse waveform from LiDAR data requires raw access to the SPAD photodetector output (timing resolution in picoseconds) rather than the processed depth map. This requires either a jailbreak, hardware teardown, or compromised firmware. The extracted pulse waveform contains: heart rate, heart rate variability (HRV), pulse transit time (correlated with blood pressure), and potentially cardiac arrhythmia signatures. Unlike QIF-T0075 (ultrasonic sonar), this is optical and directional, requiring line-of-sight but working at greater precision for skin-surface vibrations. Range is limited to ~5m (LiDAR operational range) but could be extended with higher-power laser sources.","bandsStr":"S1→S2→S3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:E/RV:F/NP:N","score":1.4,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:H/AT:P/PR:H/UI:N/VC:H/VI:N/VA:N/SC:L/SI:N/SA:N","supplemental":"S:N/AU:N/R:A/V:D","gap_group":2,"gap_summary":"Contactless cardiac waveform extraction partially captured by CVSS confidentiality; physiological privacy dimension not fully expressed"},"crossRefs":{"related_ids":["QIF-T0075","QIF-T0077","T1040","T1005"],"secondary_tactics":["QIF-N.SC","QIF-D.HV"]},"sources":["Apple iPhone LiDAR Scanner (dToF, 940nm VCSEL array, introduced iPhone 12 Pro)","Bernal et al. 2018 (Non-contact physiological monitoring using laser Doppler vibrometry)","Antognoli et al. 2020 (Contactless heart rate measurement via laser Doppler vibrometry, Sensors)","Sun et al. 2022 (Smartphone LiDAR for physiological sensing, IEEE Sensors J)","Rothberg et al. 2017 (Comprehensive review of laser Doppler vibrometry, Opt Lasers Eng)"],"tara":{"mechanism":"iPhone VCSEL LiDAR array measures skin surface micro-vibrations via Doppler shift in reflected 940nm laser; pulse waveform extracted from raw SPAD photodetector timing data","dual_use":"confirmed","clinical":{"therapeutic_analog":"Laser Doppler vibrometry for contactless cardiac monitoring","conditions":["contactless vital sign monitoring in burn units","neonatal heart rate monitoring (no adhesive sensors)","remote triage in mass casualty events","sleep lab cardiac monitoring"],"fda_status":"investigational","evidence_level":"cohort","safe_parameters":"940nm laser at Class 1 eye-safe power levels (IEC 60825-1); <1 mW accessible emission; no tissue heating","sources":["Bernal et al. 2018 (Laser Doppler vibrometry for physiology)","Antognoli et al. 2020 (Sensors, LDV heart rate)"]},"governance":{"consent_tier":"enhanced","monitoring":["LiDAR_sensor_access_audit","raw_SPAD_data_access","firmware_integrity","data_encryption_status"],"regulations":["HIPAA (if health data derived)","GDPR Art. 9 (health data)","IEC 60825-1 (laser safety Class 1)","FDA 510(k) (if marketed for cardiac monitoring)"],"data_classification":"PHI","safety_ceiling":"Laser emission within IEC 60825-1 Class 1 limits; raw SPAD data access requires hardware security module bypass; informed consent for physiological data extraction"},"engineering":{"coupling":["optical"],"parameters":{"wavelength_nm":940,"detector_type":"SPAD (single-photon avalanche diode)","timing_resolution_ps":"<100","skin_vibration_amplitude_um":"1-10","operational_range_m":"0.5-5","laser_class":"1 (eye-safe)"},"hardware":["VCSEL_array","SPAD_detector_array","time_correlator","DSP_processor"],"detection":"LiDAR sensor access logging, unexpected dToF measurements directed at people, raw SPAD data access monitoring, jailbreak detection"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"LiDAR in current iPhones and iPads","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","DI"],"cci":0.48},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.5,"gaps":["CVSS partially captures risk; neural dimensions missing","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-AUT-R-003","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0079","name":"Ear canal acoustic fingerprinting (ANC echo profiling for covert identification)","nameClinical":"Ear canal acoustic authentication for medical device access control","category":"CI","tactic":"QIF-S.FP","bands":["S1","S2","S3"],"severity":"high","status":"DEMONSTRATED","coupling":"ACOUSTIC","access":null,"classicalDetection":"Yes","quantumDetection":"No (classical acoustic resonance measurement)","description":"Active noise cancellation (ANC) earbuds already contain the complete hardware for ear canal acoustic fingerprinting: an inward-facing (feedback) microphone that measures sound inside the ear canal, an outward-facing (feedforward) microphone, and a speaker that can emit probe tones. When the speaker emits a broadband chirp or swept sine, the inward-facing microphone captures the echo profile shaped by the ear canal's unique geometry: length (~25mm), diameter (~7mm), tympanic membrane compliance, and the specific curvature of the bends. This acoustic transfer function is a biometric — NEC demonstrated it for authentication with >99% accuracy. In the attack scenario, the ANC system's existing probe tones (used for adaptive fit detection and transparency mode calibration) are leveraged to silently fingerprint the wearer without their knowledge. The earbuds know WHO is wearing them at all times. Combined with audioplethysmography (PPG via in-ear speaker/mic measuring blood volume changes), the same hardware simultaneously provides identity + heart rate: a silent surveillance pipeline requiring zero hardware modification on ANC earbuds. Attack surface: firmware update, compromised ANC calibration routine, or malicious SDK in earbud companion app.","bandsStr":"S1→S2→S3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:I/RV:F/NP:N","score":2,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N","supplemental":"S:N/AU:Y/R:A/V:D","gap_group":2,"gap_summary":"Covert biometric identification via consumer earbud partially captured by CVSS; irrevocable biometric capture dimension not fully expressed"},"crossRefs":{"related_ids":["QIF-T0032","QIF-T0036","QIF-T0073","QIF-T0075","T1040","T1556"],"secondary_tactics":["QIF-D.HV","QIF-B.IN","QIF-N.SC"]},"sources":["NEC Corporation 2016 (Ear acoustic authentication technology)","Gao et al. 2019 (EarEcho: Continuous ear canal authentication, ACM IMWUT)","Fan et al. 2021 (HeadFi: continuous earphone authentication using ear biometrics, ACM SenSys)","Apple AirPods Pro ANC system (feedforward + feedback microphones, adaptive transparency)","Akkermans et al. 2005 (Acoustic ear recognition, IEEE ICB)"],"tara":{"mechanism":"ANC earbud speaker emits probe tone; feedback microphone captures ear canal echo profile shaped by unique anatomical geometry; acoustic transfer function serves as biometric identifier","dual_use":"confirmed","clinical":{"therapeutic_analog":"Ear canal acoustic authentication for medical device access control","conditions":["hearing aid personalization (acoustic fit verification)","continuous authentication for hearing-assistive BCIs","otoacoustic emission screening (newborn hearing tests)","middle ear health monitoring (tympanometry equivalent)"],"fda_status":"none","evidence_level":"cohort","safe_parameters":"Probe tones within ANC operational range (20 Hz - 10 kHz); SPL within hearing safety limits (<85 dB); brief duration (<100ms per probe)","sources":["NEC Corporation 2016 (ear acoustic authentication)","Gao et al. 2019 (ACM IMWUT, EarEcho)"]},"governance":{"consent_tier":"enhanced","monitoring":["ANC_probe_tone_audit","biometric_data_handling","firmware_integrity","companion_app_permissions"],"regulations":["GDPR Art. 9 (biometric data)","Illinois BIPA (biometric collection without consent)","CCPA (biometric identifiers)","EU AI Act (biometric identification systems)"],"data_classification":"PII","safety_ceiling":"Acoustic probe within hearing safety limits; biometric data processed locally (not transmitted); explicit consent for identification use; ear canal biometric treated as irrevocable"},"engineering":{"coupling":["acoustic"],"parameters":{"probe_type":"broadband chirp or swept sine","frequency_range_Hz":"200-8000","probe_duration_ms":"10-100","identification_accuracy_pct":">99 (NEC demonstration)","canal_length_mm":"~25","canal_diameter_mm":"~7"},"hardware":["ANC_speaker","feedback_microphone","feedforward_microphone","DSP_processor"],"detection":"ANC probe tone frequency/timing audit, unexpected acoustic measurements outside ANC calibration cycle, biometric data transmission monitoring, firmware integrity verification"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"ANC earbuds with feedback microphones widely deployed","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","DI"],"cci":0.48},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.5,"gaps":["CVSS partially captures risk; neural dimensions missing","No FDA pathway for consumer sensor exploitation"]}},"taraAlias":"TARA-IDN-R-003","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0080","name":"Gyroscope acoustic eavesdropping (MEMS speech capture via resonant frequency aliasing)","nameClinical":"Gyroscope acoustic eavesdropping (MEMS speech capture via resonant frequency aliasing)","category":"SE","tactic":"QIF-S.RP","bands":["S1","S2","S3"],"severity":"high","status":"DEMONSTRATED","coupling":null,"access":null,"classicalDetection":"Yes","quantumDetection":"No (purely classical MEMS resonance exploit)","description":"MEMS gyroscopes in smartphones sample at 100-200 Hz, with mechanical resonant frequencies in the audible speech range (100-8000 Hz). When sound waves strike the MEMS proof mass, they induce vibrations that alias into the gyroscope output. Michalevsky et al. (2014) demonstrated that gyroscope data alone can reconstruct intelligible speech features, identify speakers, and detect spoken digits — all without microphone permission. Android allowed gyroscope access without any permission until API level 33. This creates a covert audio surveillance channel through an 'inertial' sensor that apps access freely. Combined with accelerometer data (T0081), reconstruction quality improves significantly. The attack requires no hardware modification — only a software app with motion sensor access.","bandsStr":"S1→S2→S3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:E/RV:F/NP:N","score":1.4,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:L/SI:N/SA:N","supplemental":"S:N/AU:Y/R:A/V:D","gap_group":1,"gap_summary":"Covert eavesdropping via motion sensor; CVSS confidentiality metrics apply but permission-bypass dimension not captured"},"crossRefs":{"related_ids":["QIF-T0072","QIF-T0081","QIF-T0083","T1123"],"secondary_tactics":["QIF-D.HV"]},"sources":["Michalevsky et al. 2014 (Gyrophone: Recognizing Speech from Gyroscope Signals, USENIX Security)","Zhang et al. 2017 (AccelWord: Energy-efficient voice command recognition via accelerometer)","Anand & Saxena 2018 (Speechless: Analyzing the Threat to Speech Privacy from Smartphone Motion Sensors, IEEE S&P)"],"tara":{"mechanism":"MEMS gyroscope mechanical resonance captures airborne acoustic vibrations; speech features reconstructed via signal processing of motion sensor output","dual_use":"silicon_only","clinical":null,"governance":{"consent_tier":"standard","monitoring":["motion_sensor_access_audit","gyroscope_sampling_rate_monitoring","app_permission_review"],"regulations":["ECPA (18 U.S.C. § 2511)","GDPR Art. 5","Android sensor permission policy"],"data_classification":"PII","safety_ceiling":"Motion sensor access should require explicit permission; sampling rate capped below speech-relevant frequencies when audio permission not granted"},"engineering":{"coupling":["acoustic","mechanical"],"parameters":{"gyroscope_sample_rate_Hz":"100-200","resonant_frequency_Hz":"100-8000 (MEMS-dependent)","speech_reconstruction_accuracy":"~65% digit recognition","SNR_requirement_dB":">10"},"hardware":["MEMS_gyroscope","DSP_processor"],"detection":"Gyroscope access frequency monitoring, anomalous continuous sampling patterns, app behavior analysis for motion-to-audio correlation"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"MEMS gyroscopes in all smartphones","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP"],"cci":0.12},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.5,"gaps":["No FDA pathway for consumer sensor exploitation","Software-only attack without software lifecycle standard (IEC 62304)"]}},"taraAlias":"TARA-AUD-R-001","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0081","name":"Accelerometer speech reconstruction (vibration-to-audio via surface-coupled MEMS)","nameClinical":"Accelerometer speech reconstruction (vibration-to-audio via surface-coupled MEMS)","category":"SE","tactic":"QIF-S.RP","bands":["S1","S2","S3"],"severity":"high","status":"DEMONSTRATED","coupling":null,"access":null,"classicalDetection":"Yes","quantumDetection":"No (classical vibration analysis)","description":"Modern smartphone accelerometers (ADXL345, BMI270) have sufficient sensitivity to capture speech vibrations transmitted through surfaces. When a phone lies on a table during a conversation, or is held against the ear during a call, speech vibrations propagate through the phone chassis to the MEMS accelerometer. Ba et al. (2020) used deep learning to reconstruct intelligible speech from accelerometer data alone, achieving speaker identification and keyword recognition. Unlike the gyroscope attack (T0080), accelerometers benefit from direct surface coupling — speech vibrations transmitted through desks, tables, or the user's hand provide stronger signal. Like T0080, accelerometer access required no permission on Android until recent API changes. The combination of gyroscope + accelerometer data (sensor fusion) significantly improves reconstruction quality over either sensor alone.","bandsStr":"S1→S2→S3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:E/RV:F/NP:N","score":1.4,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:L/SI:N/SA:N","supplemental":"S:N/AU:Y/R:A/V:D","gap_group":1,"gap_summary":"Eavesdropping via accelerometer; standard confidentiality metrics apply"},"crossRefs":{"related_ids":["QIF-T0072","QIF-T0080","QIF-T0083","QIF-T0087","T1123"],"secondary_tactics":["QIF-D.HV"]},"sources":["Zhang et al. 2019 (AccelEve: Eavesdropping via Accelerometers on Smartphones, IEEE S&P Workshop)","Ba et al. 2020 (Learning-based Practical Smartphone Eavesdropping with Built-in Accelerometer, NDSS)","Han et al. 2012 (ACComplice: accelerometer side channel, ACM CCS)"],"tara":{"mechanism":"MEMS accelerometer captures speech vibrations transmitted through surfaces or phone chassis; deep learning reconstructs intelligible audio from motion sensor data","dual_use":"silicon_only","clinical":null,"governance":{"consent_tier":"standard","monitoring":["accelerometer_access_audit","sensor_sampling_rate_monitoring","app_permission_review"],"regulations":["ECPA (18 U.S.C. § 2511)","GDPR Art. 5","Android sensor permission policy"],"data_classification":"PII","safety_ceiling":"Accelerometer access should require permission when sampling above speech-relevant thresholds; surface coupling mitigated by isolation mounts"},"engineering":{"coupling":["acoustic","mechanical"],"parameters":{"accelerometer_sample_rate_Hz":"100-500","sensitivity_mg":"0.1-1.0","speech_reconstruction_WER":"~30-50% (deep learning)","surface_coupling_gain_dB":"+10-20 vs airborne"},"hardware":["MEMS_accelerometer","ML_inference_engine"],"detection":"Accelerometer access pattern monitoring, anomalous continuous high-rate sampling, correlation with ambient audio events"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"MEMS accelerometers in all smartphones","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP"],"cci":0.12},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.5,"gaps":["No FDA pathway for consumer sensor exploitation","Software-only attack without software lifecycle standard (IEC 62304)"]}},"taraAlias":"TARA-AUD-R-002","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0082","name":"Ultrasonic cross-device tracking (inaudible beacon correlation for user identification)","nameClinical":"Ultrasonic cross-device tracking (inaudible beacon correlation for user identification)","category":"CI","tactic":"QIF-S.HV","bands":["S1","S2","S3"],"severity":"high","status":"CONFIRMED","coupling":"ACOUSTIC","access":null,"classicalDetection":"Yes","quantumDetection":"No (classical acoustic signaling)","description":"Advertisers and tracking firms embed inaudible ultrasonic beacons (18-22 kHz) in TV commercials, web ads, and in-store audio. Any device with microphone access (phone, tablet, smart speaker) within acoustic range can detect these beacons and report them to a tracking server. This enables cross-device user identification (linking phone, laptop, TV viewing), physical location tracking (in-store beacons), and de-anonymization of Tor/VPN users (TV ad beacons correlate with browsing sessions). Silverpush was found embedded in 234 Android apps (2017). The beacons are inaudible to humans but easily detected by consumer microphones. Combined with QIF-T0075 (ultrasonic sonar), the same frequency band serves both tracking and physiological surveillance. This technique requires no hardware modification — only software with microphone permission.","bandsStr":"S1→S2→S3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:I/RV:F/NP:N","score":2,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N","supplemental":"S:N/AU:Y/R:A/V:D","gap_group":1,"gap_summary":"Cross-device tracking via ultrasonic beacons; CVSS confidentiality metrics partially capture privacy loss"},"crossRefs":{"related_ids":["QIF-T0075","QIF-T0079","T1040","T1071"],"secondary_tactics":["QIF-D.HV","QIF-N.SC"]},"sources":["Mavroudis et al. 2017 (On the Privacy and Security of the Ultrasound Ecosystem, PoPETS)","Arp et al. 2017 (Privacy Threats through Ultrasonic Side Channels on Mobile Devices, IEEE EuroS&P)","Silverpush, Lisnr, SilverPush SDK (commercial ultrasonic tracking)"],"tara":{"mechanism":"Inaudible ultrasonic beacons (18-22 kHz) embedded in audio content detected by consumer device microphones for cross-device user tracking and location correlation","dual_use":"silicon_only","clinical":null,"governance":{"consent_tier":"standard","monitoring":["ultrasonic_spectrum_analysis","microphone_access_audit","network_beacon_correlation"],"regulations":["GDPR Art. 5 (transparency)","FTC Act Section 5","ePrivacy Directive"],"data_classification":"PII","safety_ceiling":"Ultrasonic beacon detection should be disclosed; microphone access for tracking requires explicit consent; frequency filtering above 18 kHz by default"},"engineering":{"coupling":["acoustic"],"parameters":{"beacon_frequency_kHz":"18-22","beacon_duration_ms":"50-500","detection_range_m":"1-10","encoding":"frequency-shift or amplitude modulation"},"hardware":["consumer_speaker","consumer_microphone","DSP_processor"],"detection":"Ultrasonic spectrum monitoring (18-22 kHz), microphone access auditing, network traffic analysis for beacon reporting endpoints"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Consumer speakers and microphones sufficient","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP"],"cci":0.12},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.5,"gaps":["No FDA pathway for consumer sensor exploitation","Software-only attack without software lifecycle standard (IEC 62304)"]}},"taraAlias":"TARA-SIL-R-008","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0083","name":"Acoustic keystroke inference (typing sound classification for credential extraction)","nameClinical":"Acoustic keystroke inference (typing sound classification for credential extraction)","category":"CI","tactic":"QIF-S.HV","bands":["S1","S2","S3"],"severity":"high","status":"CONFIRMED","coupling":"ACOUSTIC","access":null,"classicalDetection":"Yes","quantumDetection":"No (classical acoustic pattern recognition)","description":"Each key on a keyboard produces a subtly different acoustic signature based on its position, the mechanical structure beneath it, and the user's typing dynamics. Harrison & Matyunin (2023) achieved 95% keystroke classification accuracy using a deep learning model trained on laptop keyboard audio captured by a nearby phone. The attack works over Zoom/Skype calls (Compagno et al. 2017), enabling remote credential theft during video conferences. Attack scenarios: (1) malicious app with microphone access on the same desk, (2) nearby compromised smart speaker, (3) during a video call where keyboard sounds leak through the microphone. This technique pairs with accelerometer keystroke inference (T0087) for multi-modal confirmation, significantly reducing error rates.","bandsStr":"S1→S2→S3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:I/RV:F/NP:N","score":2,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N","supplemental":"S:N/AU:Y/R:A/V:D","gap_group":1,"gap_summary":"Credential theft via acoustic side channel; CVSS confidentiality applies well"},"crossRefs":{"related_ids":["QIF-T0080","QIF-T0081","QIF-T0087","T1056.001"],"secondary_tactics":["QIF-D.HV"]},"sources":["Harrison & Matyunin 2023 (A Practical Deep Learning-Based Acoustic Side Channel Attack on Keyboards, IEEE European Symposium on Security and Privacy Workshops)","Zhuang et al. 2009 (Keyboard Acoustic Emanations Revisited, ACM TISSEC)","Compagno et al. 2017 (Don't Skype & Type! Acoustic Eavesdropping in Voice-Over-IP, ACM CCS)"],"tara":{"mechanism":"Deep learning classification of keyboard acoustic emanations to reconstruct typed text including credentials and sensitive content","dual_use":"silicon_only","clinical":null,"governance":{"consent_tier":"standard","monitoring":["microphone_access_audit","audio_classification_detection","VoIP_audio_filtering"],"regulations":["ECPA (18 U.S.C. § 2511)","GDPR Art. 5","CFAA (credential theft)"],"data_classification":"PII","safety_ceiling":"Microphone access disclosure; VoIP clients should filter keyboard frequency bands; keystroke sounds should be suppressed in conferencing software"},"engineering":{"coupling":["acoustic"],"parameters":{"classification_accuracy_pct":">95 (nearby phone), ~60 (VoIP)","frequency_range_Hz":"1000-16000","model_type":"CNN/transformer on mel spectrograms","training_data":"~25 keystrokes per key"},"hardware":["consumer_microphone","ML_inference_engine"],"detection":"Audio stream analysis for keystroke patterns, VoIP audio filtering, acoustic noise injection for keystroke masking"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Consumer microphones sufficient","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP"],"cci":0.12},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.5,"gaps":["No FDA pathway for consumer sensor exploitation","Software-only attack without software lifecycle standard (IEC 62304)"]}},"taraAlias":"TARA-SIL-R-009","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0084","name":"Remote photoplethysmography (camera-based pulse and blood oxygen extraction)","nameClinical":"Contactless vital sign monitoring for telemedicine and patient screening","category":"SE","tactic":"QIF-S.HV","bands":["S1","S2","S3"],"severity":"high","status":"DEMONSTRATED","coupling":"OPTICAL","access":null,"classicalDetection":"Yes","quantumDetection":"No (classical optical measurement)","description":"Standard RGB webcams and phone cameras can detect the subtle skin color changes caused by blood volume pulses beneath the skin surface. Each heartbeat modulates hemoglobin concentration in facial capillaries, creating sub-pixel intensity variations in the green channel (540nm peak absorption of hemoglobin). Modern deep learning models (DeepPhys, EfficientPhys) extract heart rate, heart rate variability, breathing rate, and blood oxygen saturation from webcam video with near-clinical accuracy — even through video compression artifacts on Zoom/Teams calls. Attack scenario: any app with camera access (video call, face filter, AR app) silently extracts physiological data. The user consents to video, not to vital sign monitoring. This technique has been demonstrated at distances up to 3m with consumer cameras and works under variable ambient lighting conditions.","bandsStr":"S1→S2→S3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:E/RV:F/NP:N","score":1.4,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:N/SC:L/SI:N/SA:N","supplemental":"S:N/AU:Y/R:A/V:D","gap_group":2,"gap_summary":"Covert physiological extraction from video partially captured by CVSS; health privacy dimension not fully expressed"},"crossRefs":{"related_ids":["QIF-T0075","QIF-T0077","QIF-T0093","T1040"],"secondary_tactics":["QIF-D.HV","QIF-N.SC"]},"sources":["Verkruysse et al. 2008 (Remote plethysmographic imaging using ambient light, Optics Express)","Poh et al. 2011 (Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam, IEEE Trans Biomed Eng)","Chen & McDuff 2018 (DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks, ECCV)","Liu et al. 2023 (EfficientPhys: enabling simple, fast and accurate camera-based vitals measurement)"],"tara":{"mechanism":"RGB camera captures sub-pixel skin color variations from cardiac blood volume pulses; deep learning extracts heart rate, HRV, respiratory rate, and SpO2 from video","dual_use":"confirmed","clinical":{"therapeutic_analog":"Contactless vital sign monitoring for telemedicine and patient screening","conditions":["remote patient monitoring (telemedicine vitals)","neonatal heart rate monitoring (non-contact)","mental health stress screening (HRV analysis)","pain assessment (autonomic response detection)"],"fda_status":"investigational","evidence_level":"cohort","safe_parameters":"Standard RGB camera at ambient light; no active illumination required; operates within normal video call conditions","sources":["Poh et al. 2011 (IEEE Trans Biomed Eng, webcam vitals)","Chen & McDuff 2018 (ECCV, DeepPhys)"]},"governance":{"consent_tier":"enhanced","monitoring":["camera_access_audit","video_processing_pipeline_audit","physiological_data_extraction_detection"],"regulations":["HIPAA (if health data derived)","GDPR Art. 9 (health data)","FTC Act Section 5","EU AI Act (biometric processing)"],"data_classification":"PHI","safety_ceiling":"Camera-based vital sign extraction requires explicit consent beyond video permission; data minimization for physiological features"},"engineering":{"coupling":["optical"],"parameters":{"camera_resolution":"640x480 minimum","frame_rate_fps":">15","heart_rate_accuracy_bpm":"±2-5","SpO2_accuracy_pct":"±2-3","working_distance_m":"0.3-3"},"hardware":["RGB_camera","ML_inference_engine"],"detection":"Camera access auditing, video processing pipeline monitoring, detection of physiological feature extraction in video frames"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"RGB cameras in all smartphones","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","DI"],"cci":0.48},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.5,"gaps":["CVSS partially captures risk; neural dimensions missing","No FDA pathway for consumer sensor exploitation"]}},"taraAlias":"TARA-AUT-R-004","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0085","name":"Eye tracking cognitive state inference (gaze pattern analysis for attention and intent profiling)","nameClinical":"Eye tracking for neurological assessment and cognitive rehabilitation","category":"EX","tactic":"QIF-S.HV","bands":["S1","S2","N3","N7"],"severity":"critical","status":"DEMONSTRATED","coupling":"OPTICAL","access":null,"classicalDetection":"Yes","quantumDetection":"Enhanced (QI coherence metric detects unauthorized cognitive profiling if deployed)","description":"Eye tracking hardware is now standard in AR/VR headsets (Apple Vision Pro, Meta Quest Pro, PSVR2) and available as peripherals for laptops (Tobii). Gaze patterns reveal far more than where someone looks: pupil dilation indicates cognitive load and arousal, saccade patterns reveal reading comprehension and attention, fixation duration maps interest and engagement, and smooth pursuit movements indicate prediction and anticipation. Research has demonstrated extraction of: sexual orientation, political affiliation, cognitive disorders (ADHD, dyslexia, autism), emotional state, deception, and even personality traits from eye tracking data alone. In VR/AR headsets, eye tracking runs continuously for foveated rendering (a legitimate performance optimization), creating an always-on cognitive surveillance channel. The user consents to eye tracking for UI interaction, not for cognitive profiling. This is the closest consumer-sensor analog to neural eavesdropping without any BCI hardware.","bandsStr":"S1→S2→N3→N7","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:H/CD:H/CV:I/RV:F/NP:N","score":3.4,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:L/VA:N/SC:H/SI:L/SA:N","supplemental":"S:P/AU:Y/R:A/V:D","gap_group":3,"gap_summary":"Cognitive state inference from eye tracking — mental privacy violation not expressible in CVSS; closest consumer analog to neural eavesdropping"},"crossRefs":{"related_ids":["QIF-T0074","QIF-T0041","QIF-T0036","T1005"],"secondary_tactics":["QIF-C.EX","QIF-D.HV","QIF-M.SV"]},"sources":["Katsini et al. 2020 (The Role of Eye Gaze in Security and Privacy Applications, ACM Computing Surveys)","Sluganovic et al. 2018 (Using Reflexive Eye Movements for Fast Challenge-Response Authentication, USENIX Security)","Apple Vision Pro eye tracking (visionOS gaze-based interaction)","Meta Quest Pro eye tracking (Meta Presence Platform)","Liebling & Preibusch 2014 (Privacy of Web Search via Eye Tracking, PoPETS)"],"tara":{"mechanism":"Eye tracking hardware in AR/VR headsets captures gaze patterns, pupil dilation, saccades, and fixations; ML models infer cognitive states, personality traits, and intent","dual_use":"confirmed","clinical":{"therapeutic_analog":"Eye tracking for neurological assessment and cognitive rehabilitation","conditions":["ADHD diagnosis (saccade pattern analysis)","autism spectrum screening (gaze pattern biomarkers)","traumatic brain injury assessment","Alzheimer's early detection (reading pattern changes)"],"fda_status":"cleared","evidence_level":"RCT","safe_parameters":"IR illumination within IEC 62471 limits; gaze data processed locally; cognitive inferences require explicit consent","sources":["Katsini et al. 2020 (ACM Computing Surveys, eye gaze in security)","Sluganovic et al. 2018 (USENIX Security, reflexive eye movements)"]},"governance":{"consent_tier":"IRB","monitoring":["eye_tracking_data_access_audit","cognitive_inference_pipeline_audit","data_retention_limits","purpose_limitation_enforcement"],"regulations":["GDPR Art. 9 (biometric data)","GDPR Art. 22 (automated decision-making)","EU AI Act (high-risk biometric)","Illinois BIPA","proposed neurorights legislation"],"data_classification":"sensitive_neural","safety_ceiling":"Eye tracking cognitive inference requires explicit opt-in beyond foveated rendering consent; purpose limitation mandatory; right to cognitive liberty"},"engineering":{"coupling":["optical"],"parameters":{"sampling_rate_Hz":"60-120","accuracy_degrees":"0.5-1.0","pupil_dilation_resolution_mm":"0.01","cognitive_state_inference_latency_ms":"<500"},"hardware":["IR_LED_illuminator","eye_tracking_camera","ML_inference_engine","AR_VR_headset"],"detection":"Eye tracking data access audit logging, cognitive inference model detection, anomalous data retention patterns, purpose limitation enforcement"},"dsm5":{"primary":[{"code":"F82","name":"Developmental Coordination Disorder","confidence":"established"},{"code":"F84","name":"Pervasive developmental disorders","confidence":"established"},{"code":"F20","name":"Schizophrenia Spectrum","confidence":"established"},{"code":"F32","name":"Major Depressive Disorder","confidence":"established"},{"code":"F90","name":"ADHD","confidence":"established"},{"code":"F42","name":"OCD","confidence":"established"}],"secondary":[{"code":"F30","name":"Manic episode","confidence":"probable"},{"code":"F41.1","name":"Generalized Anxiety Disorder","confidence":"probable"},{"code":"F01","name":"Vascular dementia","confidence":"established"},{"code":"F43","name":"PTSD / Trauma","confidence":"established"},{"code":"F80","name":"Communication Disorders","confidence":"established"},{"code":"F60","name":"Personality Disorders","confidence":"probable"},{"code":"F63","name":"Impulse-Control Disorders","confidence":"probable"},{"code":"F98.4","name":"Stereotyped movement disorders","confidence":"established"}],"risk_class":"direct","cluster":"cognitive_psychotic","pathway":"N7 (PFC/M1) → executive function; N3 (cerebellar cortex/deep cerebellar nuclei) → motor coordination","niss_correlation":"CR:H,CD:H,CV:I → cognitive/psychotic cluster"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Eye tracking in Vision Pro, Quest, consumer webcams","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","DI"],"cci":1.8},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":false,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.4,"gaps":["CVSS cannot express neural-specific impacts","No FDA pathway for consumer sensor exploitation"]}},"taraAlias":"TARA-VIS-R-001","taraDomainPrimary":"COG","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0086","name":"Ambient light sensor side-channel exfiltration (screen content inference via reflected light)","nameClinical":"Ambient light sensor side-channel exfiltration (screen content inference via reflected light)","category":"SE","tactic":"QIF-S.HV","bands":["S1","S2","S3"],"severity":"medium","status":"DEMONSTRATED","coupling":"OPTICAL","access":null,"classicalDetection":"Yes","quantumDetection":"No (classical optical side channel)","description":"Ambient light sensors (ALS) in smartphones and tablets are low-resolution photometers (typically 16-bit, 10-100 Hz) that measure environmental illumination for auto-brightness. Since the ALS is near the display, it also captures light reflected back from the display itself and from nearby surfaces illuminated by the display. This creates a side channel: the ALS output correlates with screen content. While the ALS cannot reconstruct a full image, it can distinguish between dark and light screens, detect page scrolling patterns, identify video content by temporal light signatures, and in some cases infer text content via character-level luminance patterns. Crucially, ALS access requires no permission on most mobile platforms — it's treated as a low-risk environmental sensor. This makes it an unrestricted exfiltration channel for screen activity patterns.","bandsStr":"S1→S2→S3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:P/RV:F/NP:N","score":0.7,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:H/AT:N/PR:N/UI:N/VC:L/VI:N/VA:N/SC:N/SI:N/SA:N","supplemental":"S:N/AU:N/R:A/V:D","gap_group":1,"gap_summary":"Low-bandwidth side channel; CVSS confidentiality captures the data loss adequately"},"crossRefs":{"related_ids":["QIF-T0084","T1005","T1040"],"secondary_tactics":["QIF-D.HV"]},"sources":["Zhang & Lian 2024 (Eavesdropping on Controller Area Network via Ambient Light Sensor, ACM CCS)","Spreitzer et al. 2018 (Systematic Classification of Side-Channel Attacks: A Case Study for Mobile Devices, IEEE Communications Surveys)","Mosenia et al. 2017 (PinMe: Tracking a Smartphone User around the World, IEEE Trans Multi-Scale Computing Systems)"],"tara":{"mechanism":"Ambient light sensor captures display-reflected light variations to infer screen content, scrolling patterns, and user activity without requiring camera or screen capture permissions","dual_use":"silicon_only","clinical":null,"governance":{"consent_tier":"standard","monitoring":["ALS_access_frequency_audit","sensor_data_exfiltration_detection"],"regulations":["GDPR Art. 5","ECPA","ePrivacy Directive"],"data_classification":"PII","safety_ceiling":"ALS sampling rate should be limited when screen content could be inferred; permission model should gate high-frequency ALS access"},"engineering":{"coupling":["optical"],"parameters":{"ALS_resolution_bits":"16","sampling_rate_Hz":"10-100","content_inference_accuracy":"Activity classification (~80%), text inference (limited)","requires_permission":"No (most platforms)"},"hardware":["ambient_light_sensor","DSP_processor"],"detection":"ALS access frequency monitoring, correlation analysis between ALS data and known screen content patterns"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Ambient light sensors in all smartphones","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP"],"cci":0.1},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.5,"gaps":["No FDA pathway for consumer sensor exploitation","Software-only attack without software lifecycle standard (IEC 62304)"]}},"taraAlias":"TARA-SIL-R-010","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0087","name":"Accelerometer keystroke inference (touchscreen tap localization for PIN/password recovery)","nameClinical":"Accelerometer keystroke inference (touchscreen tap localization for PIN/password recovery)","category":"CI","tactic":"QIF-S.HV","bands":["S1","S2","S3"],"severity":"high","status":"CONFIRMED","coupling":null,"access":null,"classicalDetection":"Yes","quantumDetection":"No (classical vibration analysis)","description":"When a user taps on a touchscreen, the phone tilts slightly depending on the tap location relative to the device's center of mass. The accelerometer and gyroscope capture these micro-tilts, and the pattern differs for each key position on the virtual keyboard. Owusu et al. (2012) demonstrated 4-digit PIN recovery from accelerometer data alone, and Miluzzo et al. (2012) showed that tap signatures are consistent enough for user identification. The attack works because: (1) different screen positions produce distinct tilt vectors, (2) typing rhythm provides temporal constraints, and (3) language models constrain character sequences. Combined with acoustic keystroke inference (T0083), the multi-modal approach achieves near-perfect accuracy. Since motion sensor access traditionally required no permission, any app could silently capture PIN entry.","bandsStr":"S1→S2→S3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:I/RV:F/NP:N","score":2,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N","supplemental":"S:N/AU:Y/R:A/V:D","gap_group":1,"gap_summary":"Credential theft via motion sensors; CVSS confidentiality metrics apply well"},"crossRefs":{"related_ids":["QIF-T0081","QIF-T0083","QIF-T0088","T1056.001"],"secondary_tactics":["QIF-D.HV","QIF-B.IN"]},"sources":["Owusu et al. 2012 (ACCessory: Password Inference using Accelerometers on Smartphones, ACM HotMobile)","Cai & Chen 2011 (TouchLogger: Inferring Keystrokes on Touch Screen from Smartphone Motion, USENIX HotSec)","Miluzzo et al. 2012 (TapPrints: Your Finger Taps Have Fingerprints, ACM MobiSys)"],"tara":{"mechanism":"Accelerometer and gyroscope capture micro-tilt patterns from touchscreen taps; ML models localize tap positions to recover PINs, passwords, and typed text","dual_use":"silicon_only","clinical":null,"governance":{"consent_tier":"standard","monitoring":["motion_sensor_access_audit","tap_pattern_detection","concurrent_keyboard_sensor_access"],"regulations":["CFAA","ECPA","GDPR Art. 5","Android/iOS sensor permission policy"],"data_classification":"PII","safety_ceiling":"Motion sensor access during keyboard input should be restricted; sensor data should not be accessible concurrent with authentication UI"},"engineering":{"coupling":["mechanical"],"parameters":{"PIN_recovery_accuracy_pct":"~70-80 (4-digit PIN)","accelerometer_sample_rate_Hz":"100-200","requires_training":"per-device calibration helps","fusion_with_T0083":"near-perfect accuracy"},"hardware":["MEMS_accelerometer","MEMS_gyroscope","ML_inference_engine"],"detection":"Motion sensor access correlation with keyboard display, anomalous high-rate sampling during authentication, sensor permission auditing"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"MEMS accelerometers in all smartphones","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP"],"cci":0.12},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.5,"gaps":["No FDA pathway for consumer sensor exploitation","Software-only attack without software lifecycle standard (IEC 62304)"]}},"taraAlias":"TARA-SIL-R-011","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0088","name":"Gait biometric identification (IMU-based walking pattern fingerprint for persistent tracking)","nameClinical":"Gait analysis for rehabilitation and neurological monitoring","category":"CI","tactic":"QIF-S.FP","bands":["S1","S2","S3"],"severity":"high","status":"CONFIRMED","coupling":null,"access":null,"classicalDetection":"Yes","quantumDetection":"No (classical motion pattern analysis)","description":"Every person has a unique gait signature determined by limb length ratios, joint flexibility, muscle strength distribution, and neurological motor control patterns. Smartphone IMU sensors (accelerometer + gyroscope) carried in a pocket capture this signature with high fidelity — Muaaz & Mayrhofer (2017) achieved >95% identification accuracy across 175 subjects. The gait signature is: (1) difficult to spoof (requires altering unconscious motor patterns), (2) captured passively (phone in pocket during normal walking), (3) persistent across sessions and devices, and (4) capturable without any explicit permission. Gait biometrics enable persistent user tracking even when other identifiers (cookies, device IDs, face) are unavailable. Combined with T0089 (neurological profiling), gait data also reveals health conditions affecting motor control.","bandsStr":"S1→S2→S3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:I/RV:F/NP:N","score":2,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N","supplemental":"S:N/AU:Y/R:A/V:D","gap_group":2,"gap_summary":"Persistent biometric tracking via gait; irrevocable biometric dimension not fully expressed in CVSS"},"crossRefs":{"related_ids":["QIF-T0079","QIF-T0089","QIF-T0091","QIF-T0093","T1040"],"secondary_tactics":["QIF-D.HV","QIF-N.SC"]},"sources":["Muaaz & Mayrhofer 2017 (Smartphone-based Gait Recognition: From Authentication to Imitation, IEEE Trans Mobile Computing)","Ngo et al. 2014 (The Largest Inertial Sensor-Based Gait Database, Pattern Recognition)","Sprager & Juric 2015 (Inertial Sensor-Based Gait Recognition: A Review, Sensors)"],"tara":{"mechanism":"Smartphone IMU sensors capture unique walking patterns determined by biomechanics and neurological motor control; ML models fingerprint individuals for persistent tracking","dual_use":"confirmed","clinical":{"therapeutic_analog":"Gait analysis for rehabilitation and neurological monitoring","conditions":["Parkinson's disease gait monitoring","fall risk assessment in elderly","post-stroke rehabilitation tracking","orthopedic recovery monitoring"],"fda_status":"cleared","evidence_level":"cohort","safe_parameters":"Passive IMU recording during normal ambulation; no active stimulation; data processed locally","sources":["Sprager & Juric 2015 (Sensors, gait recognition review)","Ngo et al. 2014 (Pattern Recognition, gait database)"]},"governance":{"consent_tier":"enhanced","monitoring":["IMU_continuous_access_audit","gait_template_storage_audit","biometric_data_handling"],"regulations":["GDPR Art. 9 (biometric data)","Illinois BIPA","CCPA (biometric identifiers)","EU AI Act (biometric identification)"],"data_classification":"PII","safety_ceiling":"Gait biometric extraction requires explicit consent; templates treated as irrevocable biometric; data minimization mandatory"},"engineering":{"coupling":["mechanical"],"parameters":{"identification_accuracy_pct":">95","IMU_sample_rate_Hz":"50-100","template_stability_days":">30","walking_sample_required_steps":"~20-50"},"hardware":["MEMS_accelerometer","MEMS_gyroscope","ML_inference_engine"],"detection":"Continuous IMU access monitoring, gait template computation detection, biometric data transmission monitoring"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"IMU sensors in all smartphones and wearables","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","DI"],"cci":0.48},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.5,"gaps":["CVSS partially captures risk; neural dimensions missing","No FDA pathway for consumer sensor exploitation"]}},"taraAlias":"TARA-MOT-R-001","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0089","name":"Tremor and movement neurological profiling (IMU-based motor disorder detection and health inference)","nameClinical":"Remote Parkinson's monitoring and neurological screening","category":"SE","tactic":"QIF-S.HV","bands":["S1","S2","N1","N7"],"severity":"critical","status":"DEMONSTRATED","coupling":null,"access":null,"classicalDetection":"Yes","quantumDetection":"Enhanced (QI coherence metric detects neurological anomalies if deployed)","description":"Smartphone IMU sensors can detect pathological tremor patterns characteristic of neurological conditions: Parkinson's resting tremor (4-6 Hz), essential tremor (8-12 Hz), cerebellar tremor (3-5 Hz), and physiological tremor changes from medication, fatigue, or substance use. The mPower study (Bot et al. 2016) enrolled 16,000 participants and demonstrated that smartphone sensor data alone can distinguish Parkinson's patients from healthy controls with >90% accuracy. Beyond tremor, fine motor control degradation (touchscreen interaction patterns, typing dynamics) reveals cognitive decline, medication effects, intoxication levels, and fatigue. This is covert neurological diagnosis without the subject's knowledge or consent — the phone becomes a continuous neurological monitor. The data reveals protected health information about neurological conditions, substance use, and cognitive function from sensors that require no permission.","bandsStr":"S1→S2→N1→N7","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:L/CD:L/CV:I/RV:F/NP:N","score":2.7,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N","supplemental":"S:P/AU:Y/R:A/V:D","gap_group":3,"gap_summary":"Covert neurological diagnosis from consumer sensors — health condition revelation and cognitive state inference not expressible in CVSS"},"crossRefs":{"related_ids":["QIF-T0074","QIF-T0088","QIF-T0085","T1005"],"secondary_tactics":["QIF-D.HV","QIF-N.SC","QIF-C.EX"]},"sources":["Arora et al. 2015 (Detecting and monitoring the symptoms of Parkinson's disease using smartphones, J Med Internet Res)","Bot et al. 2016 (mPower: a smartphone study of Parkinson disease, Scientific Data)","Giancardo et al. 2016 (Computer keyboard interaction as an indicator of early Parkinson's disease, Scientific Reports)","Adams et al. 2017 (Tremor detection from smartphone accelerometer data, IEEE EMBC)"],"tara":{"mechanism":"Smartphone IMU sensors detect pathological tremor frequencies and fine motor control degradation to infer neurological conditions, medication effects, and cognitive state","dual_use":"confirmed","clinical":{"therapeutic_analog":"Remote Parkinson's monitoring and neurological screening","conditions":["Parkinson's disease symptom tracking (mPower study)","essential tremor monitoring","multiple sclerosis motor assessment","medication effect monitoring (levodopa response tracking)"],"fda_status":"investigational","evidence_level":"cohort","safe_parameters":"Passive IMU recording; no active stimulation; data processed per HIPAA; informed consent for neurological inference","sources":["Bot et al. 2016 (Scientific Data, mPower study)","Arora et al. 2015 (J Med Internet Res, smartphone Parkinson's detection)"]},"governance":{"consent_tier":"IRB","monitoring":["IMU_continuous_access_audit","tremor_analysis_detection","health_inference_pipeline_audit"],"regulations":["HIPAA","GDPR Art. 9 (health data)","ADA (disability disclosure)","GINA (genetic information)","EU AI Act (high-risk health AI)"],"data_classification":"PHI","safety_ceiling":"Neurological inference from consumer sensors requires explicit informed consent; health condition predictions must be validated clinically; no discriminatory use"},"engineering":{"coupling":["mechanical"],"parameters":{"tremor_frequency_bands_Hz":"3-12","PD_detection_accuracy_pct":">90","IMU_sample_rate_Hz":"50-200","minimum_recording_duration_s":"10-30"},"hardware":["MEMS_accelerometer","MEMS_gyroscope","ML_inference_engine"],"detection":"Tremor analysis algorithm detection, continuous IMU sampling monitoring, health inference model output auditing"},"dsm5":{"primary":[{"code":"F45","name":"Somatoform disorders","confidence":"established"},{"code":"F44.4","name":"Conversion Disorder","confidence":"established"},{"code":"F20","name":"Schizophrenia Spectrum","confidence":"established"},{"code":"F32","name":"Major Depressive Disorder","confidence":"established"},{"code":"F90","name":"ADHD","confidence":"established"},{"code":"F42","name":"OCD","confidence":"established"}],"secondary":[{"code":"F82","name":"Developmental Coordination Disorder","confidence":"probable"},{"code":"F30","name":"Manic episode","confidence":"established"},{"code":"F43","name":"PTSD / Trauma","confidence":"established"},{"code":"F80","name":"Communication Disorders","confidence":"established"},{"code":"F60","name":"Personality Disorders","confidence":"probable"},{"code":"F63","name":"Impulse-Control Disorders","confidence":"probable"},{"code":"F01","name":"Vascular dementia","confidence":"established"},{"code":"F98.4","name":"Stereotyped movement disorders","confidence":"established"}],"risk_class":"direct","cluster":"motor_neurocognitive","pathway":"N7 (PFC/M1) → executive function; N1 (spinal cord) → reflexes","niss_correlation":"CV:I → motor/neurocognitive cluster"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"IMU sensors in all smartphones and wearables","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","MI","DI"],"cci":1.35},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":false,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.4,"gaps":["CVSS cannot express neural-specific impacts","No FDA pathway for consumer sensor exploitation"]}},"taraAlias":"TARA-MOT-R-002","taraDomainPrimary":"COG","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0090","name":"WiFi CSI passive body sensing (through-wall vital signs, 3D pose reconstruction, respiratory and gait biometric inference via dedicated or commodity WiFi hardware)","nameClinical":"Contactless vital sign monitoring for sleep studies, elder care, and post-surgical recovery","category":"SE","tactic":"QIF-S.HV","bands":["S1","S2","S3"],"severity":"critical","status":"DEMONSTRATED","coupling":"RF","access":null,"classicalDetection":"Yes","quantumDetection":"No (classical RF propagation analysis)","description":"WiFi Channel State Information (CSI) captures the multipath propagation characteristics between WiFi transmitter and receiver. Human body movements — including breathing (chest wall motion ~5mm), heartbeat (body surface vibration ~0.1mm), and walking — modulate the WiFi signal propagation paths. Two attack profiles: (1) DEDICATED HARDWARE — modified firmware on CSI-capable APs enables through-wall sensing (2-3 standard walls), 3D pose reconstruction, and multi-person vital sign extraction at up to 8m range. (2) CONSUMER-GRADE — standard WiFi chipsets (Intel 5300, Atheros, ESP32) with CSI-enabled drivers extract respiratory rate (±1 bpm at 3m) and gait identity (93% single-person, 78% multi-person) without dedicated hardware. Respiratory modulation: phase shift Δφ = 4π×Δd/λ ≈ 0.4-1.2 radians at 5 GHz (chest displacement 4-12mm). Gait produces Doppler shifts f_d = 2v×cos(θ)/λ ≈ 40 Hz at walking speed. CSI matrix: H(f,t) ∈ C^(N_tx × N_rx × N_sub). CRITICAL REGULATORY GAP: Respiratory rate = PHI under HIPAA (45 CFR 160.103) when linked to individual. Gait biometric = special category data under GDPR Art. 9. No consent mechanism exists for incidental WiFi CSI health data collection — a router collecting CSI for 'network optimization' simultaneously collects respiratory data from everyone in range. This is passive radar using existing infrastructure.","bandsStr":"S1→S2→S3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:I/RV:F/NP:N","score":2,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:L/AT:P/PR:H/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N","supplemental":"S:N/AU:Y/R:A/V:D","gap_group":2,"gap_summary":"Through-wall passive surveillance via WiFi; health privacy (HIPAA PHI), behavioral biometric (GDPR Art. 9), and regulatory consent gap dimensions not expressible in CVSS. Consumer-grade variant (PR:N/AT:N) has lower barrier to entry."},"crossRefs":{"related_ids":["QIF-T0075","QIF-T0080","QIF-T0081","QIF-T0084","QIF-T0088","QIF-T0093","QIF-T0098","QIF-T0101","T1040","T1557"],"secondary_tactics":["QIF-D.HV","QIF-N.SC","QIF-S.RP","QIF-S.FP"]},"sources":["Liu et al. 2019 (Wireless Sensing for Human Activity: A Survey, IEEE Communications Surveys)","Liu et al. 2015 (WiFi respiration monitoring)","Zeng et al. 2020 (FarSense: pushing the range limit of WiFi-based respiration sensing, ACM MobiSys)","Wang et al. 2017 (TensorBeat: Tensor Decomposition for Monitoring Multiperson Breathing Beats with Commodity WiFi, ACM Trans Intelligent Systems and Technology)","Wang et al. 2016 (WiFi-based gait recognition)","Jiang et al. 2020 (Towards 3D Human Pose Construction Using WiFi, ACM MobiCom)","Ali et al. 2015 (WiKey: keystroke recognition from WiFi CSI, MobiCom)","Ma et al. 2019 (WiFi sensing with CSI: a survey, ACM Computing Surveys)"],"tara":{"mechanism":"WiFi OFDM subcarrier amplitude and phase modulation by human body movement, respiration, and gait; works through walls without any device on the target; both dedicated CSI hardware and commodity WiFi chipsets enable contactless physiological surveillance","dual_use":"confirmed","clinical":{"therapeutic_analog":"Contactless vital sign monitoring for sleep studies, elder care, and post-surgical recovery","conditions":["contactless sleep apnea detection (Liu et al. 2015, demonstrated ±1 bpm)","elderly fall detection and activity monitoring (no wearable required)","post-surgical respiration monitoring without chest bands (burn patients)","PTSD and anxiety monitoring via nocturnal breathing pattern analysis","COPD exacerbation early warning via respiratory pattern changes","Parkinson's gait analysis for medication timing optimization","smart home health sensing"],"fda_status":"investigational","evidence_level":"cohort","safe_parameters":"Standard WiFi power levels (100 mW EIRP, < FCC Part 15 limits); passive sensing only (no additional RF emission); informed consent for monitoring","sources":["Liu et al. 2019 (IEEE, WiFi sensing survey)","Liu et al. 2015 (WiFi respiration, demonstrated ±1 bpm)","Zeng et al. 2020 (ACM MobiSys, FarSense)","Wang et al. 2017 (WiFi fall detection)"]},"governance":{"consent_tier":"enhanced","monitoring":["CSI_extraction_detection","router_firmware_integrity","unusual_WiFi_traffic_patterns","respiratory_data_access_log","multi_person_anonymization_check"],"regulations":["HIPAA (respiratory rate = PHI when linked to individual)","GDPR Art. 9 (health data, behavioral biometrics)","ECPA (electronic surveillance)","Fourth Amendment (US, through-wall surveillance)","FCC Part 15 (WiFi emissions)","EU AI Act","proposed neurorights legislation (cognitive state inference)"],"data_classification":"PHI","safety_ceiling":"Through-wall sensing requires explicit consent from all monitored persons; router firmware integrity mandatory; CSI extraction should be detectable; informed consent for health data derivation; data retention limits; anonymization for incidental collection; no CSI health data without explicit opt-in"},"engineering":{"coupling":["electromagnetic"],"parameters":{"WiFi_standard":"802.11n/ac/ax","wifi_frequency_GHz":"2.4 or 5","CSI_subcarriers":"30-256 (52 for 802.11n, 256 for 802.11ac)","CSI_sampling_rate_Hz":"100-1000","breathing_detection_range_m":"1-8","through_wall_capability":"2-3 standard walls (dedicated hardware)","heart_rate_accuracy_bpm":"±3-5 (dedicated), ±1 at 3m (consumer-grade respiration)","gait_id_accuracy_percent":"93 (single-person), 78 (multi-person)","respiratory_phase_shift_rad":"0.4-1.2 (at 5 GHz)","gait_doppler_Hz":"~40 (at 5 GHz, walking)"},"hardware":["WiFi_AP_with_CSI_support (dedicated)","Intel_5300_NIC_or_ESP32_or_Nexmon (consumer-grade)","modified_firmware","signal_processing_backend"],"detection":"CSI extraction monitoring on WiFi chipset, unusual AP firmware, anomalous WiFi traffic volume or patterns, respiratory-band filtering (0.1-0.5 Hz) in WiFi processing pipeline, RF sensing countermeasures"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — respiratory/gait data does not traverse neural pathway. Chains to N-domain if respiratory patterns used for cognitive state inference (stress detection).","niss_correlation":"Silicon-only technique — no diagnostic mapping. CV:I reflects through-wall surveillance and health data collection without consent."}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"WiFi CSI supported by Intel 5300, ESP32, Nexmon","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","DI"],"cci":0.6},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.5,"gaps":["CVSS partially captures risk; neural dimensions missing","No FDA pathway for consumer sensor exploitation"]}},"taraAlias":"TARA-AUT-R-005","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0091","name":"BLE physical-layer device fingerprinting (radio frequency imperfection tracking)","nameClinical":"BLE physical-layer device fingerprinting (radio frequency imperfection tracking)","category":"CI","tactic":"QIF-S.FP","bands":["S1","S2","S3"],"severity":"high","status":"DEMONSTRATED","coupling":"RF","access":null,"classicalDetection":"Yes","quantumDetection":"No (classical RF fingerprinting)","description":"Every Bluetooth Low Energy (BLE) transmitter has unique analog imperfections in its radio hardware: carrier frequency offset (CFO), I/Q imbalance, power amplifier nonlinearity, and phase noise characteristics. These imperfections are manufacturing artifacts that are stable, unique per device, and impossible to change via software — they are the RF equivalent of a fingerprint. Becker et al. (2022) demonstrated that BLE physical-layer fingerprinting can track devices even when using MAC address randomization (the privacy feature specifically designed to prevent tracking). This defeats Apple's and Google's BLE privacy protections. Attack scenario: passive BLE receivers at strategic locations (malls, airports, streets) fingerprint passing devices. The user's phone continuously advertises BLE (for AirDrop, Find My, COVID exposure notifications), and each advertisement carries the device's unchangeable RF fingerprint. This enables persistent location tracking despite all software-level privacy measures.","bandsStr":"S1→S2→S3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:I/RV:F/NP:N","score":2,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N","supplemental":"S:N/AU:Y/R:A/V:D","gap_group":2,"gap_summary":"Persistent device tracking defeating privacy controls; irrevocable hardware fingerprint dimension not fully expressed in CVSS"},"crossRefs":{"related_ids":["QIF-T0079","QIF-T0088","QIF-T0082","T1040","T1120"],"secondary_tactics":["QIF-D.HV","QIF-N.SC"]},"sources":["Becker et al. 2022 (Tracking Anonymized Bluetooth Devices, PoPETS)","Das et al. 2018 (Tracking Mobile Web Users Through Motion Sensors, NDSS)","Ramsey et al. 2020 (BLE Device Tracking via Physical Layer Fingerprinting, IEEE CNS)"],"tara":{"mechanism":"Passive BLE receiver extracts unique physical-layer radio imperfections (CFO, I/Q imbalance) from BLE advertisements to track devices despite MAC address randomization","dual_use":"silicon_only","clinical":null,"governance":{"consent_tier":"enhanced","monitoring":["BLE_fingerprinting_detection","RF_scanning_infrastructure_audit","location_data_collection_audit"],"regulations":["GDPR Art. 5 (purpose limitation)","ePrivacy Directive","CCPA","ECPA"],"data_classification":"PII","safety_ceiling":"Physical-layer fingerprinting defeats software privacy measures; regulatory frameworks need updating for hardware-level tracking; BLE chipset randomization of analog characteristics needed"},"engineering":{"coupling":["electromagnetic"],"parameters":{"CFO_resolution_Hz":"<100","identification_accuracy_pct":">90","tracking_persistence":"permanent (hardware-determined)","BLE_advertisement_interval_ms":"20-10240"},"hardware":["SDR_receiver_or_modified_BLE_chipset","signal_processing_backend"],"detection":"Detection of passive BLE scanning infrastructure, RF environment monitoring, anomalous BLE receiver deployments"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"SDR receivers commercially available","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP"],"cci":0.24},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.4,"gaps":["CVSS partially captures risk; neural dimensions missing","No FDA pathway for consumer sensor exploitation","Software-only attack without software lifecycle standard (IEC 62304)"]}},"taraAlias":"TARA-SIL-R-012","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0092","name":"Thermal facial stress and emotion inference (IR thermography for autonomic nervous system state extraction)","nameClinical":"Thermal imaging for pain assessment and autonomic function testing","category":"SE","tactic":"QIF-S.HV","bands":["S1","S2","N7"],"severity":"high","status":"DEMONSTRATED","coupling":"OPTICAL","access":null,"classicalDetection":"Yes","quantumDetection":"No (classical thermal imaging)","description":"The autonomic nervous system modulates facial skin temperature through vasoconstriction/vasodilation in response to stress, cognitive load, deception, arousal, and emotional states. Periorbital temperature (around the eyes) drops during stress as blood redirects to core muscles. Nasal tip temperature correlates with cognitive load. Forehead temperature maps to anxiety. Thermal cameras (LWIR, 8-14 µm) capture these patterns contactlessly. While consumer thermal cameras are not yet standard in phones, they are available as accessories (FLIR One, Seek Thermal), integrated into some laptops (for presence detection), and standard in many security/surveillance systems. Pavlidis et al. (2002) demonstrated thermal imaging as a polygraph alternative. As thermal sensors become cheaper and more integrated into consumer devices, this becomes a passive emotion/stress surveillance channel.","bandsStr":"S1→S2→N7","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:L/CD:L/CV:E/RV:F/NP:N","score":2,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:H/VI:N/VA:N/SC:L/SI:N/SA:N","supplemental":"S:N/AU:Y/R:A/V:D","gap_group":3,"gap_summary":"Emotional state inference from thermal imaging — psychological privacy dimension not expressible in CVSS"},"crossRefs":{"related_ids":["QIF-T0077","QIF-T0084","QIF-T0085","T1040"],"secondary_tactics":["QIF-D.HV","QIF-C.EX"]},"sources":["Engert et al. 2014 (Exploring the Use of Thermal Imaging to Assess Stress, PLOS ONE)","Abdelrahman et al. 2017 (Cognitive Heat: Exploring the Usage of Thermal Imaging to Unobtrusively Estimate Cognitive Load, ACM IMWUT)","Pavlidis et al. 2002 (Thermal Image Analysis for Polygraph Testing, IEEE Engineering in Medicine and Biology)"],"tara":{"mechanism":"Thermal IR camera captures facial temperature distribution modulated by autonomic nervous system; ML models infer stress, cognitive load, emotion, and deception from thermal patterns","dual_use":"confirmed","clinical":{"therapeutic_analog":"Thermal imaging for pain assessment and autonomic function testing","conditions":["pain assessment (objective thermal correlates)","anxiety disorder monitoring (periorbital temperature)","PTSD arousal detection","neuropathy assessment (thermal regulation dysfunction)"],"fda_status":"cleared","evidence_level":"cohort","safe_parameters":"Passive thermal imaging (no radiation emitted); LWIR 8-14 µm detection only; standard room temperature conditions","sources":["Engert et al. 2014 (PLOS ONE, thermal stress imaging)","Pavlidis et al. 2002 (IEEE EMB, thermal polygraph)"]},"governance":{"consent_tier":"enhanced","monitoring":["thermal_camera_access_audit","emotion_inference_pipeline_audit","data_retention_limits"],"regulations":["GDPR Art. 9 (health data)","EU AI Act (emotion recognition systems)","HIPAA","Illinois BIPA"],"data_classification":"PHI","safety_ceiling":"Emotion inference from thermal imaging requires explicit consent; EU AI Act may ban emotion recognition in certain contexts; data minimization mandatory"},"engineering":{"coupling":["optical"],"parameters":{"wavelength_um":"8-14 (LWIR)","temperature_resolution_K":"0.05-0.1","frame_rate_Hz":"9-60","spatial_resolution_pixels":"160x120 to 640x480","working_distance_m":"0.5-5"},"hardware":["LWIR_thermal_camera","ML_inference_engine"],"detection":"Thermal camera access auditing, emotion inference model detection, unexpected IR sensor activation"},"dsm5":{"primary":[{"code":"F20","name":"Schizophrenia Spectrum","confidence":"established"},{"code":"F32","name":"Major Depressive Disorder","confidence":"established"},{"code":"F90","name":"ADHD","confidence":"established"},{"code":"F42","name":"OCD","confidence":"established"}],"secondary":[{"code":"F30","name":"Manic episode","confidence":"established"},{"code":"F43","name":"PTSD / Trauma","confidence":"established"},{"code":"F80","name":"Communication Disorders","confidence":"established"},{"code":"F60","name":"Personality Disorders","confidence":"probable"},{"code":"F63","name":"Impulse-Control Disorders","confidence":"probable"},{"code":"F01","name":"Vascular dementia","confidence":"established"},{"code":"F98.4","name":"Stereotyped movement disorders","confidence":"established"}],"risk_class":"direct","cluster":"cognitive_psychotic","pathway":"N7 (PFC/M1) → executive function","niss_correlation":"CV:E → cognitive/psychotic cluster"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"LWIR thermal cameras commercially available","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","DI"],"cci":0.96},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":false,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.4,"gaps":["CVSS cannot express neural-specific impacts","No FDA pathway for consumer sensor exploitation"]}},"taraAlias":"TARA-EMO-R-001","taraDomainPrimary":"COG","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0093","name":"PPG pulse waveform biometric identification (cardiac signature fingerprinting via wearable optical sensor)","nameClinical":"PPG-based cardiovascular health monitoring","category":"CI","tactic":"QIF-S.FP","bands":["S1","S2","S3"],"severity":"high","status":"DEMONSTRATED","coupling":"OPTICAL","access":null,"classicalDetection":"Yes","quantumDetection":"No (classical photoplethysmography)","description":"Photoplethysmography (PPG) sensors in smartwatches and fitness trackers measure blood volume changes through green LED light reflected from the wrist. The PPG waveform shape is unique per individual — determined by cardiac output, arterial stiffness, vessel geometry, and autonomic tone. Biswas et al. (2019) demonstrated >98% identification accuracy using deep learning on PPG waveforms. Unlike heart rate (a single number), the full PPG waveform is a rich biometric containing: pulse amplitude, dicrotic notch depth, systolic/diastolic ratio, pulse transit time, and waveform morphology. This biometric is continuously captured by any wearable with a heart rate sensor (Apple Watch, Fitbit, Galaxy Watch, Oura Ring). The user consents to heart rate monitoring, not to biometric identification from their cardiac waveform. Combined with T0088 (gait) and T0079 (ear canal), the attacker has three independent biometric channels from consumer devices.","bandsStr":"S1→S2→S3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:I/RV:F/NP:N","score":2,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N","supplemental":"S:N/AU:Y/R:A/V:D","gap_group":2,"gap_summary":"Cardiac biometric extraction from wearable; irrevocable biometric dimension not fully expressed in CVSS"},"crossRefs":{"related_ids":["QIF-T0077","QIF-T0079","QIF-T0084","QIF-T0088","T1040"],"secondary_tactics":["QIF-D.HV","QIF-N.SC"]},"sources":["Biswas et al. 2019 (CorNET: Deep Learning Framework for PPG-Based Heart Rate Estimation and Biometric Identification, IEEE Trans Biomed Eng)","Yadav et al. 2018 (Evaluation of PPG biometrics for authentication, IEEE ICB)","Kavsaoglu et al. 2023 (PPG-based biometric identification: A comprehensive review, Expert Systems with Applications)"],"tara":{"mechanism":"Wearable PPG sensor captures unique cardiac pulse waveform morphology determined by cardiovascular physiology; deep learning extracts biometric identity from waveform features","dual_use":"confirmed","clinical":{"therapeutic_analog":"PPG-based cardiovascular health monitoring","conditions":["atrial fibrillation detection (Apple Watch FDA clearance)","blood pressure estimation (pulse wave analysis)","sleep apnea screening (SpO2 + pulse waveform)","vascular stiffness assessment"],"fda_status":"cleared","evidence_level":"RCT","safe_parameters":"Green LED at standard wearable power levels; Class 1 device; continuous wear monitoring","sources":["Biswas et al. 2019 (IEEE Trans Biomed Eng, PPG biometrics)","Apple 2020 (Apple Watch AFib detection, FDA De Novo clearance)"]},"governance":{"consent_tier":"enhanced","monitoring":["PPG_raw_data_access_audit","biometric_template_storage","waveform_export_monitoring"],"regulations":["GDPR Art. 9 (biometric data)","Illinois BIPA","CCPA (biometric identifiers)","HIPAA (cardiovascular health data)"],"data_classification":"PII","safety_ceiling":"PPG biometric identification requires explicit consent beyond health monitoring; cardiac waveform templates treated as irrevocable biometric; data minimization mandatory"},"engineering":{"coupling":["optical"],"parameters":{"LED_wavelength_nm":"530 (green)","sampling_rate_Hz":"25-250","identification_accuracy_pct":">98","template_stability":"high (cardiovascular structure is stable)"},"hardware":["green_LED","photodetector","wearable_housing","ML_inference_engine"],"detection":"PPG raw data access auditing, biometric template computation detection, unusual waveform data export"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"PPG sensors in all smartwatches","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","DI"],"cci":0.48},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.5,"gaps":["CVSS partially captures risk; neural dimensions missing","No FDA pathway for consumer sensor exploitation"]}},"taraAlias":"TARA-AUT-R-006","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0094","name":"Magnetometer speaker-leakage eavesdropping (magnetic field emanation capture from speaker voice coils)","nameClinical":"Magnetometer speaker-leakage eavesdropping (magnetic field emanation capture from speaker voice coils)","category":"SE","tactic":"QIF-S.RP","bands":["S1","S2","S3"],"severity":"high","status":"DEMONSTRATED","coupling":"ELECTROMAGNETIC","access":null,"classicalDetection":"Yes","quantumDetection":"No (classical electromagnetic emanation)","description":"Speaker voice coils are electromagnets — when driven by audio current, they produce proportional magnetic field emanations. Smartphone magnetometers (used for compass/navigation) are sensitive enough to detect these emanations from nearby speakers, earbuds, or headphones. Zhang et al. (2020) demonstrated that a smartphone's magnetometer placed within 10-20 cm of earbuds can reconstruct the audio being played, including speech. This creates an eavesdropping channel through magnetic emanations rather than acoustic leakage — it works even when the audio is not audible (noise-canceling headphones, low volume). Matyunin et al. (2019) showed that magnetometer data can also fingerprint websites and applications by their characteristic audio/vibration patterns. Magnetometer access requires no permission on most platforms, making this an unrestricted side channel.","bandsStr":"S1→S2→S3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:E/RV:F/NP:N","score":1.4,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:P/AC:L/AT:P/PR:N/UI:N/VC:H/VI:N/VA:N/SC:L/SI:N/SA:N","supplemental":"S:N/AU:N/R:A/V:D","gap_group":1,"gap_summary":"Eavesdropping via magnetic emanation; CVSS confidentiality metrics apply adequately"},"crossRefs":{"related_ids":["QIF-T0072","QIF-T0080","QIF-T0081","T1040","T1123"],"secondary_tactics":["QIF-D.HV"]},"sources":["Matyunin et al. 2019 (MagneticSpy: Exploiting Magnetometer in Mobile Devices for Website and Application Fingerprinting, ACM WiSec)","Guri et al. 2020 (MAGNETO: Covert Channel between Air-Gapped Systems and Nearby Smartphones via CPU-Generated Magnetic Fields)","Zhang et al. 2020 (MagAttack: Eavesdropping on Headphone Magnetic Leakage)"],"tara":{"mechanism":"Smartphone magnetometer captures electromagnetic emanations from nearby speaker voice coils to reconstruct audio content without acoustic coupling","dual_use":"silicon_only","clinical":null,"governance":{"consent_tier":"standard","monitoring":["magnetometer_access_audit","EM_emanation_monitoring","proximity_detection"],"regulations":["ECPA (18 U.S.C. § 2511)","GDPR Art. 5","FCC Part 15 (incidental emissions)"],"data_classification":"PII","safety_ceiling":"Magnetometer access should require permission when used at high sampling rates; speaker shielding reduces magnetic emanations; distance increases attenuation rapidly"},"engineering":{"coupling":["electromagnetic"],"parameters":{"magnetometer_sensitivity_uT":"0.01-0.1","effective_range_cm":"10-20","audio_reconstruction_quality":"intelligible speech at close range","requires_permission":"No (most platforms)"},"hardware":["MEMS_magnetometer","signal_processing_backend"],"detection":"Magnetometer access frequency monitoring, correlation with nearby speaker activity, EM shielding of speaker voice coils"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"MEMS magnetometers in smartphones","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP"],"cci":0.12},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.5,"gaps":["No FDA pathway for consumer sensor exploitation","Software-only attack without software lifecycle standard (IEC 62304)"]}},"taraAlias":"TARA-SIL-R-013","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0095","name":"Acoustic-to-neural profiling pipeline (consumer earbud escalation from audio to cognitive exploitation)","nameClinical":"Integrated hearing health + cognitive monitoring earbuds","category":"EX","tactic":"QIF-S.CH","bands":["S1","S2","S3","I0","N1","N7"],"severity":"critical","status":"EMERGING","coupling":"ACOUSTIC","access":null,"classicalDetection":"Partial","quantumDetection":"Enhanced (QI coherence metric detects unauthorized neural data acquisition)","description":"This technique documents the complete escalation chain from a single compromised consumer earbud to cognitive profiling. The chain proceeds: (1) T0072 — Speaker-to-mic reprogramming gives ambient audio eavesdropping, (2) T0079 — Ear canal acoustic fingerprinting silently identifies the wearer, (3) T0073 — Modified earbud with conductive ear tip captures in-ear EEG, (4) T0074 — Longitudinal EEG data feeds ML model for cognitive profiling. The end state: a single pair of compromised earbuds provides identity + ambient audio + continuous neural telemetry + personalized cognitive vulnerability profile — all from a device the target voluntarily wears for hours daily. Each step in the chain has been independently demonstrated or is emerging. The complete chain represents a consumer-device pathway to cognitive exploitation without any traditional BCI hardware. This is the canonical example of why the S-domain exists: consumer sensors as a pre-BCI attack surface.","bandsStr":"S1→S2→S3→I0→N1→N7","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:H/CD:H/CV:I/RV:F/NP:T","score":4,"severity":"medium","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:P/AC:L/AT:P/PR:N/UI:N/VC:H/VI:L/VA:N/SC:H/SI:L/SA:N","supplemental":"S:P/AU:Y/R:A/V:C","gap_group":3,"gap_summary":"Multi-stage escalation from consumer sensor to cognitive profiling — cumulative privacy and cognitive liberty violation far exceeds any single CVSS metric"},"crossRefs":{"related_ids":["QIF-T0072","QIF-T0073","QIF-T0074","QIF-T0079"],"secondary_tactics":["QIF-S.RP","QIF-S.FP","QIF-S.HV","QIF-C.EX","QIF-D.HV"]},"sources":["Chain synthesis from: QIF-T0072 → QIF-T0073 → QIF-T0074 → QIF-T0079","Kaveh et al. 2020 (In-ear EEG, IEEE Trans Biomed Eng)","Martinovic et al. 2012 (BCI side channels, USENIX Security)","NEC Corporation 2016 (Ear acoustic authentication)"],"tara":{"mechanism":"Multi-stage escalation chain: speaker repurposing → ear canal fingerprinting → in-ear EEG capture → ML-based cognitive profiling, all from a single compromised consumer earbud","dual_use":"confirmed","clinical":{"therapeutic_analog":"Integrated hearing health + cognitive monitoring earbuds","conditions":["hearing aid with cognitive load monitoring","seizure detection + audio therapy delivery","neurofeedback training via earbuds"],"fda_status":"investigational","evidence_level":"preclinical","safe_parameters":"Each chain stage within individual safety bounds; cumulative data collection requires enhanced consent","sources":["Kaveh et al. 2020 (IEEE Trans Biomed Eng)","NEC 2016 (ear acoustic authentication)"]},"governance":{"consent_tier":"IRB","monitoring":["multi_stage_chain_detection","cumulative_data_correlation_audit","neural_data_access_monitoring"],"regulations":["All regulations from T0072-T0074 apply cumulatively","proposed neurorights legislation","EU AI Act (high-risk biometric + cognitive)"],"data_classification":"sensitive_neural","safety_ceiling":"Full chain represents cognitive sovereignty violation; each escalation stage should trigger independent consent; supply chain integrity for earbuds critical"},"engineering":{"coupling":["acoustic","electromagnetic","galvanic","computational"],"parameters":{"chain_stages":4,"escalation_time":"minutes (hardware) to weeks (cognitive model training)","data_types":"audio + identity + EEG + cognitive profile","single_device":true},"hardware":["modified_consumer_earbud","conductive_ear_tip","biopotential_amplifier","BLE_transceiver","cloud_ML_backend"],"detection":"Multi-stage chain detection requires monitoring acoustic, biometric, neural, and cognitive data pipelines simultaneously; supply chain attestation most effective"},"dsm5":{"primary":[{"code":"F43.2","name":"Adjustment Disorder","confidence":"established"},{"code":"F45","name":"Somatoform disorders","confidence":"established"},{"code":"F44.4","name":"Conversion Disorder","confidence":"established"},{"code":"F20","name":"Schizophrenia Spectrum","confidence":"established"},{"code":"F32","name":"Major Depressive Disorder","confidence":"established"},{"code":"F90","name":"ADHD","confidence":"established"},{"code":"F42","name":"OCD","confidence":"established"}],"secondary":[{"code":"F82","name":"Developmental Coordination Disorder","confidence":"probable"},{"code":"F30","name":"Manic episode","confidence":"established"},{"code":"F43","name":"PTSD / Trauma","confidence":"established"},{"code":"F80","name":"Communication Disorders","confidence":"established"},{"code":"F60","name":"Personality Disorders","confidence":"probable"},{"code":"F63","name":"Impulse-Control Disorders","confidence":"probable"},{"code":"F01","name":"Vascular dementia","confidence":"established"},{"code":"F98.4","name":"Stereotyped movement disorders","confidence":"established"}],"risk_class":"direct","cluster":"motor_neurocognitive","pathway":"N7 (PFC/M1) → executive function; I0 (electrode-tissue boundary) → measurement","niss_correlation":"CR:H,CD:H,CV:I → motor/neurocognitive cluster"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Consumer earbud pipeline components all available","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","DI","PC"],"cci":1.8},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.2,"gaps":["CVSS cannot express neural-specific impacts","No FDA pathway for consumer sensor exploitation"]}},"taraAlias":"TARA-COG-R-011","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0096","name":"Multi-modal biometric fusion attack (cross-sensor identity correlation for persistent tracking)","nameClinical":"Multi-modal patient identification for medication safety","category":"CI","tactic":"QIF-S.CH","bands":["S1","S2","S3"],"severity":"critical","status":"EMERGING","coupling":null,"access":null,"classicalDetection":"Yes","quantumDetection":"No (classical pattern fusion)","description":"By fusing biometric signatures from multiple consumer sensors — ear canal acoustics (T0079), gait pattern (T0088), BLE RF fingerprint (T0091), PPG waveform (T0093), and eye tracking (T0085 if VR/AR) — an attacker creates a multi-modal biometric profile that is virtually impossible to evade. Each individual biometric can potentially be disrupted (change earbuds, alter gait, disable Bluetooth), but the fusion of 3+ biometric channels provides robust identification even if individual channels are degraded. The fusion operates at the feature level (concatenated feature vectors) or decision level (majority voting across classifiers). This technique weaponizes the ubiquity of consumer sensors: the average person carries 10+ sensors across phone, watch, and earbuds. The combination creates a biometric surveillance net that no single privacy measure can defeat.","bandsStr":"S1→S2→S3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:I/RV:F/NP:N","score":2,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N","supplemental":"S:N/AU:Y/R:U/V:D","gap_group":2,"gap_summary":"Multi-modal biometric fusion creating near-irrevocable identity profile; aggregate biometric dimension exceeds individual CVSS confidentiality"},"crossRefs":{"related_ids":["QIF-T0079","QIF-T0085","QIF-T0088","QIF-T0091","QIF-T0093"],"secondary_tactics":["QIF-S.FP","QIF-D.HV","QIF-N.SC"]},"sources":["Chain synthesis from: QIF-T0079 (ear canal) + QIF-T0088 (gait) + QIF-T0091 (BLE) + QIF-T0093 (PPG)","Ross et al. 2006 (Handbook of Multibiometrics, Springer)","Hadid et al. 2015 (Biometrics Systems Under Spoofing Attack: An Evaluation Methodology and Lessons Learned, IEEE Signal Processing Magazine)"],"tara":{"mechanism":"Fusion of biometric signatures from multiple consumer sensors (acoustic, IMU, RF, optical) to create robust multi-modal identity profile resistant to individual channel disruption","dual_use":"confirmed","clinical":{"therapeutic_analog":"Multi-modal patient identification for medication safety","conditions":["patient identification in hospitals (multi-factor biometric)","elderly person identification in care facilities","clinical trial participant verification"],"fda_status":"none","evidence_level":"preclinical","safe_parameters":"Each sensor within individual safety bounds; fusion layer is computational only","sources":["Ross et al. 2006 (Handbook of Multibiometrics)","Hadid et al. 2015 (IEEE SPM, biometric spoofing)"]},"governance":{"consent_tier":"IRB","monitoring":["cross_sensor_correlation_detection","multi_modal_fusion_audit","biometric_data_aggregation_monitoring"],"regulations":["GDPR Art. 9 (biometric data)","Illinois BIPA","EU AI Act (biometric identification)","CCPA"],"data_classification":"PII","safety_ceiling":"Multi-modal biometric fusion creates near-irrevocable identity profile; exceeds single-biometric consent requirements; aggregate biometric data requires enhanced protection"},"engineering":{"coupling":["computational"],"parameters":{"modalities":"3-5 (acoustic, IMU, RF, optical, cardiac)","fusion_strategy":"feature-level or decision-level","identification_accuracy_pct":">99 (3+ modalities)","robustness_to_channel_loss":"maintains ID with any 2 of 4 channels"},"hardware":["multi_sensor_consumer_devices","ML_fusion_engine"],"detection":"Cross-sensor data correlation monitoring, multi-modal biometric computation detection, aggregation audit logging"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Multi-sensor consumer devices widely deployed","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","DI","IDA"],"cci":1.35},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.4,"gaps":["CVSS partially captures risk; neural dimensions missing","No FDA pathway for consumer sensor exploitation"]}},"taraAlias":"TARA-IDN-R-004","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0097","name":"Cross-device physiological correlation (phone + watch + earbuds comprehensive health profiling)","nameClinical":"Multi-device remote patient monitoring for chronic disease management","category":"SE","tactic":"QIF-S.CH","bands":["S1","S2","S3","N7"],"severity":"critical","status":"THEORETICAL","coupling":null,"access":null,"classicalDetection":"Yes","quantumDetection":"Enhanced (QI coherence metric detects unauthorized health profiling if deployed)","description":"The average consumer now carries 3+ sensor-equipped devices: smartphone (accelerometer, gyroscope, magnetometer, barometer, camera, microphone, ambient light, proximity, WiFi, BLE), smartwatch (PPG, accelerometer, gyroscope, SpO2, skin temperature, ECG), and earbuds (microphone, accelerometer, proximity, potentially EEG). By correlating physiological data across all devices simultaneously, an attacker builds a comprehensive health profile far exceeding what any single device captures: cardiac health (watch PPG + phone rPPG), respiratory health (phone ultrasonic + WiFi CSI), neurological health (earbud IMU tremor + phone motor patterns), mental health (watch HRV + earbud audio context + phone screen activity), and metabolic health (activity + sleep + heart rate patterns). The correlation also eliminates single-sensor noise and improves accuracy. This technique doesn't require hardware modification — only software-level data aggregation across apps on a shared platform (e.g., iOS HealthKit, Google Health Connect).","bandsStr":"S1→S2→S3→N7","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:L/CD:L/CV:I/RV:F/NP:N","score":2.7,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N","supplemental":"S:P/AU:Y/R:A/V:D","gap_group":3,"gap_summary":"Comprehensive cross-device health profiling — aggregate health privacy violation far exceeds individual sensor CVSS scores"},"crossRefs":{"related_ids":["QIF-T0075","QIF-T0084","QIF-T0089","QIF-T0090","QIF-T0093"],"secondary_tactics":["QIF-S.HV","QIF-D.HV"]},"sources":["Chain synthesis from: T0075 (ultrasonic) + T0084 (rPPG) + T0089 (tremor) + T0090 (WiFi CSI) + T0093 (PPG)","Majumder et al. 2017 (Wearable Sensors for Remote Health Monitoring, Sensors)","Dunn et al. 2021 (Wearables and the medical revolution, Personalized Medicine)"],"tara":{"mechanism":"Cross-device physiological data correlation across phone + watch + earbuds to build comprehensive health profile exceeding single-device capability","dual_use":"confirmed","clinical":{"therapeutic_analog":"Multi-device remote patient monitoring for chronic disease management","conditions":["heart failure decompensation prediction (multi-sensor)","diabetes management (activity + sleep + heart rate correlation)","mental health monitoring (multi-modal behavioral markers)","clinical trial endpoint monitoring"],"fda_status":"investigational","evidence_level":"cohort","safe_parameters":"Each device within individual safety bounds; data aggregation requires platform-level health data consent","sources":["Majumder et al. 2017 (Sensors, wearable remote health monitoring)","Dunn et al. 2021 (Personalized Medicine, wearables)"]},"governance":{"consent_tier":"IRB","monitoring":["cross_device_data_correlation_audit","health_data_aggregation_monitoring","platform_API_access_audit"],"regulations":["HIPAA","GDPR Art. 9","EU AI Act (high-risk health AI)","21 CFR Part 11","proposed digital health regulations"],"data_classification":"PHI","safety_ceiling":"Cross-device health profiling exceeds individual device consent; platform-level health data access requires comprehensive informed consent; purpose limitation for health inference"},"engineering":{"coupling":["computational"],"parameters":{"devices_correlated":"3+ (phone, watch, earbuds)","sensor_modalities":"10+ across devices","health_domains_covered":"cardiac, respiratory, neurological, mental, metabolic","data_aggregation":"platform API (HealthKit/Health Connect) or direct app access"},"hardware":["smartphone","smartwatch","consumer_earbuds","cloud_aggregation_backend"],"detection":"Cross-app data sharing monitoring, health data API access auditing, unusual data aggregation patterns across device ecosystem"},"dsm5":{"primary":[{"code":"F20","name":"Schizophrenia Spectrum","confidence":"established"},{"code":"F32","name":"Major Depressive Disorder","confidence":"established"},{"code":"F90","name":"ADHD","confidence":"established"},{"code":"F42","name":"OCD","confidence":"established"}],"secondary":[{"code":"F30","name":"Manic episode","confidence":"established"},{"code":"F43","name":"PTSD / Trauma","confidence":"established"},{"code":"F80","name":"Communication Disorders","confidence":"established"},{"code":"F60","name":"Personality Disorders","confidence":"probable"},{"code":"F63","name":"Impulse-Control Disorders","confidence":"probable"},{"code":"F01","name":"Vascular dementia","confidence":"established"},{"code":"F98.4","name":"Stereotyped movement disorders","confidence":"established"}],"risk_class":"direct","cluster":"cognitive_psychotic","pathway":"N7 (PFC/M1) → executive function","niss_correlation":"CV:I → cognitive/psychotic cluster"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Cross-device correlation via consumer devices","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","DI","IDA"],"cci":2.25},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.2,"gaps":["CVSS cannot express neural-specific impacts","No FDA pathway for consumer sensor exploitation","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-AUT-R-007","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0098","name":"WiFi + camera passive surveillance fusion (through-wall presence detection with visual identification)","nameClinical":"Ambient assisted living for elderly monitoring","category":"SE","tactic":"QIF-S.CH","bands":["S1","S2","S3"],"severity":"critical","status":"EMERGING","coupling":"RF","access":null,"classicalDetection":"Yes","quantumDetection":"No (classical signal fusion)","description":"WiFi CSI (T0090) provides through-wall presence detection, vital signs, and coarse pose estimation but cannot visually identify targets. Cameras (T0084) provide visual identification and remote PPG but require line of sight. By fusing WiFi CSI and camera data, an attacker achieves persistent surveillance that combines the strengths of both: WiFi tracks targets through walls and identifies them by body shape/gait, while cameras provide visual identification when line of sight is available. The fusion enables: handoff tracking (camera identifies person entering a building, WiFi CSI tracks them inside), activity recognition (WiFi CSI classifies activity, camera confirms), and vital sign correlation (WiFi breathing rate + camera heart rate). Zhao et al. (2018) demonstrated that WiFi signals alone can reconstruct 2D human poses comparable to visual skeleton tracking. This creates a surveillance system that requires no devices on the target and works through physical barriers.","bandsStr":"S1→S2→S3","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:I/RV:F/NP:N","score":2,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:L/AT:P/PR:H/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N","supplemental":"S:N/AU:Y/R:A/V:D","gap_group":2,"gap_summary":"Through-wall surveillance fusion; physical privacy intrusion and persistent tracking exceed standard CVSS confidentiality"},"crossRefs":{"related_ids":["QIF-T0084","QIF-T0090","T1040","T1557"],"secondary_tactics":["QIF-S.HV","QIF-D.HV","QIF-N.SC"]},"sources":["Chain synthesis from: QIF-T0084 (rPPG) + QIF-T0090 (WiFi CSI)","Li et al. 2019 (Wi-Fi See It All: Generative Adversarial Network Augmented Transparent Sensing, ACM SenSys)","Zhao et al. 2018 (Through-Wall Human Pose Estimation Using Radio Signals, CVPR)"],"tara":{"mechanism":"Fusion of WiFi CSI through-wall sensing with camera-based visual identification for persistent surveillance that works through physical barriers","dual_use":"confirmed","clinical":{"therapeutic_analog":"Ambient assisted living for elderly monitoring","conditions":["elderly fall detection (through-wall + visual confirmation)","dementia patient monitoring in care facilities","post-surgical recovery activity monitoring"],"fda_status":"none","evidence_level":"preclinical","safe_parameters":"Standard WiFi power levels; standard camera; passive sensing only; informed consent from all monitored persons","sources":["Zhao et al. 2018 (CVPR, RF-based pose estimation)","Li et al. 2019 (ACM SenSys, WiFi transparent sensing)"]},"governance":{"consent_tier":"IRB","monitoring":["WiFi_CSI_extraction_detection","camera_surveillance_audit","fusion_system_deployment_detection"],"regulations":["Fourth Amendment (US, through-wall surveillance)","ECPA","GDPR Art. 5","EU AI Act (biometric surveillance)"],"data_classification":"PII","safety_ceiling":"Through-wall surveillance constitutes warrantless search in most jurisdictions; requires explicit consent from all monitored persons; deployment detection mechanisms needed"},"engineering":{"coupling":["electromagnetic","optical"],"parameters":{"WiFi_detection_range_m":"1-10 through walls","camera_identification_range_m":"1-50 line of sight","pose_estimation_accuracy":"comparable to visual skeleton tracking (WiFi alone)","fusion_strategy":"temporal correlation + identity handoff"},"hardware":["WiFi_AP_with_CSI_support","RGB_camera","fusion_backend"],"detection":"CSI extraction monitoring, camera placement auditing, fusion system network traffic analysis"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"WiFi CSI + RGB cameras both available","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","DI"],"cci":0.9},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.4,"gaps":["CVSS partially captures risk; neural dimensions missing","No FDA pathway for consumer sensor exploitation"]}},"taraAlias":"TARA-SIL-R-014","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0099","name":"Consumer-sensor-to-BCI kill chain escalation (pre-implant reconnaissance and cognitive priming via consumer devices)","nameClinical":"Pre-surgical neurological baseline assessment for BCI implant calibration","category":"EX","tactic":"QIF-S.CH","bands":["S1","S2","S3","I0","N1","N7"],"severity":"critical","status":"THEORETICAL","coupling":null,"access":null,"classicalDetection":"Partial","quantumDetection":"Enhanced (QI coherence metric monitors S-domain-to-BCI transition integrity)","description":"This capstone technique documents the full S-domain-to-BCI escalation kill chain: how consumer sensor exploitation serves as reconnaissance and preparation for subsequent BCI attacks. The chain proceeds in phases: (1) RECON — Consumer sensors (phone, watch, earbuds) establish behavioral baseline: gait patterns (T0088), cardiac signature (T0093), neurological profile (T0089), cognitive patterns (T0085 if VR/AR). (2) FINGERPRINT — Multi-modal biometric fusion (T0096) creates persistent identity profile. (3) PROFILE — Cross-device correlation (T0097) builds comprehensive health/cognitive baseline. (4) ESCALATE — When the target receives a BCI (medical implant, consumer neural interface), the attacker's pre-existing profile informs: optimal attack parameters for neural injection (calibrated to individual's neural baseline), personalized evasion of anomaly detection (trained on their 'normal'), and targeted cognitive exploitation (leveraging known cognitive vulnerabilities). The S-domain reconnaissance makes BCI attacks more effective, more targeted, and harder to detect. This technique represents the strategic justification for the entire S-domain: consumer sensors are the advance scout for future BCI exploitation.","bandsStr":"S1→S2→S3→I0→N1→N7","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:H/CD:H/CV:I/RV:T/NP:T","score":4.7,"severity":"medium","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:N/AC:H/AT:P/PR:L/UI:N/VC:H/VI:H/VA:L/SC:H/SI:H/SA:L","supplemental":"S:P/AU:Y/R:U/V:C","gap_group":3,"gap_summary":"Full consumer-to-BCI kill chain — the culmination of all S-domain techniques feeding BCI exploitation; cognitive sovereignty violation at maximum scope; no CVSS equivalent"},"crossRefs":{"related_ids":["QIF-T0001","QIF-T0003","QIF-T0041","QIF-T0074","QIF-T0088","QIF-T0089","QIF-T0093","QIF-T0095","QIF-T0096","QIF-T0097"],"secondary_tactics":["QIF-S.RP","QIF-S.FP","QIF-S.HV","QIF-B.IN","QIF-C.EX","QIF-N.SC","QIF-D.HV"]},"sources":["Chain synthesis from: S-domain techniques → core TARA BCI techniques","Yuste et al. 2017 (Four ethical priorities for neurotechnologies and AI, Nature)","Ienca & Andorno 2017 (Towards new human rights in the age of neuroscience, Life Sciences Society and Policy)","Landau et al. 2020 (Mind Reading: An Idea Whose Time Has Come?)"],"tara":{"mechanism":"Full S-domain-to-BCI escalation: consumer sensor reconnaissance builds behavioral/physiological/cognitive baseline that informs and optimizes subsequent BCI attack parameters","dual_use":"confirmed","clinical":{"therapeutic_analog":"Pre-surgical neurological baseline assessment for BCI implant calibration","conditions":["BCI implant pre-surgical planning (behavioral baseline)","neural interface calibration (cognitive baseline)","personalized neuroprosthetic fitting","rehabilitation baseline assessment"],"fda_status":"investigational","evidence_level":"preclinical","safe_parameters":"Pre-implant assessment conducted under clinical protocol; informed consent for all data collection phases; data used only for therapeutic calibration","sources":["Yuste et al. 2017 (Nature, neurotechnology ethics)","Ienca & Andorno 2017 (neurorights)"]},"governance":{"consent_tier":"IRB","monitoring":["cross_domain_escalation_detection","pre_BCI_reconnaissance_audit","consumer_to_neural_data_pipeline_monitoring"],"regulations":["All consumer sensor regulations + all BCI regulations apply cumulatively","proposed neurorights legislation","EU AI Act","HIPAA"],"data_classification":"sensitive_neural","safety_ceiling":"Consumer sensor data collected pre-BCI must not persist into BCI context without explicit re-consent; S-domain-to-BCI data handoff is a critical consent boundary; cognitive sovereignty requires clean break between consumer and neural data domains"},"engineering":{"coupling":["acoustic","electromagnetic","optical","mechanical","computational"],"parameters":{"recon_phase_duration":"weeks to months","escalation_phases":4,"data_types":"behavioral + physiological + biometric + cognitive","BCI_attack_effectiveness_improvement":"estimated 2-5x with S-domain recon"},"hardware":["consumer_phone","smartwatch","earbuds","BCI_implant_or_interface"],"detection":"S-domain-to-BCI transition monitoring is the critical detection point; consumer data aggregation auditing; BCI calibration data provenance verification"},"dsm5":{"primary":[{"code":"F43.2","name":"Adjustment Disorder","confidence":"established"},{"code":"F45","name":"Somatoform disorders","confidence":"established"},{"code":"F44.4","name":"Conversion Disorder","confidence":"established"},{"code":"F20","name":"Schizophrenia Spectrum","confidence":"established"},{"code":"F32","name":"Major Depressive Disorder","confidence":"established"},{"code":"F90","name":"ADHD","confidence":"established"},{"code":"F42","name":"OCD","confidence":"established"}],"secondary":[{"code":"F82","name":"Developmental Coordination Disorder","confidence":"probable"},{"code":"F30","name":"Manic episode","confidence":"established"},{"code":"F43","name":"PTSD / Trauma","confidence":"established"},{"code":"F80","name":"Communication Disorders","confidence":"established"},{"code":"F60","name":"Personality Disorders","confidence":"probable"},{"code":"F63","name":"Impulse-Control Disorders","confidence":"probable"},{"code":"F01","name":"Vascular dementia","confidence":"established"},{"code":"F98.4","name":"Stereotyped movement disorders","confidence":"established"}],"risk_class":"direct","cluster":"motor_neurocognitive","pathway":"N7 (PFC/M1) → executive function; I0 (electrode-tissue boundary) → measurement","niss_correlation":"CR:H,CD:H,CV:I → motor/neurocognitive cluster"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Consumer sensor kill chain components all exist today","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","DI","IDA","PC"],"cci":2.25},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":false,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.2,"gaps":["CVSS cannot express neural-specific impacts","No FDA pathway for consumer sensor exploitation","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-COG-R-012","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0100","name":"Neural steganographic encoding (inaudible audio watermarking for covert neural command/biofingerprint channel)","nameClinical":"Auditory brainstem response (ABR) audiometry, ASSR-based hearing threshold estimation, tinnitus masking therapy","category":"CI","tactic":"QIF-S.CH","bands":["S3","S1","I0","N1","N4","N7"],"severity":"low","status":"PLAUSIBLE","coupling":"ACOUSTIC","access":null,"classicalDetection":"Yes","quantumDetection":"No","description":"Embedding data in audio signals below the human hearing threshold (>18 kHz ultrasonic or <20 Hz infrasonic) or within psychoacoustic masking bands of audible content. The encoded signal is imperceptible to the listener but decodable by a receiver with knowledge of the encoding scheme. In the adversarial case, the hidden channel carries covert commands, tracking identifiers, or subliminal cues targeting auditory processing pathways (e.g., triggering pre-conditioned responses or influencing decision-making). In the protective case, the same physics enables steganographic embedding of neural biofingerprints for authentication — the signal evokes a measurable auditory evoked potential (AEP) unique to the individual's cochlear geometry and auditory cortex response, functioning as a biometric watermark. Initial enrollment/profiling phase required to capture individual's unique AEP response. Practical constraints include target device's ability to accurately reproduce encoded frequencies and effective transmission range (1-10m). Psychoacoustic masking threshold follows: T_mask(f) = L_masker - (sf × |f - f_masker|) where sf is the spreading factor (~25 dB/Bark on upper slope, ~10 dB/Bark on lower slope). Auditory steady-state response (ASSR) at 40 Hz provides the mechanism for neural biofingerprint verification. Proves TARA's cross-domain dual-use thesis: same physics operates as attack vector, defense mechanism, and therapeutic tool.","bandsStr":"S3→S1→I0→N1→N4→N7","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:L/CD:L/CV:P/RV:F/NP:N","score":1.4,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:H/AT:P/PR:N/UI:P/VC:L/VI:N/VA:N/SC:N/SI:N/SA:N","supplemental":"S:N/AU:N/R:A/V:D","gap_group":1,"gap_summary":"Covert channel partially captured by CVSS confidentiality; neural biofingerprint and subliminal cognitive dimensions not expressible"},"crossRefs":{"related_ids":["QIF-T0072","QIF-T0073","QIF-T0074","QIF-T0079"],"secondary_tactics":["QIF-S.CH","QIF-S.FP","QIF-N.SC"]},"sources":["Deshotels 2014 (Inaudible sound as a covert channel)","Carrara 2015 (Ultrasonic data transmission)","Fletcher 1940 (Psychoacoustic masking curves)","Galambos 1981 (40 Hz auditory steady-state response)","Persinger 2003 (Infrasonic effects on vestibular system)","Chaieb et al. 2015 (Neuropsychologia, binaural beat effects)"],"tara":{"mechanism":"Inaudible audio carriers (ultrasonic 18-22 kHz or infrasonic <20 Hz) or psychoacoustically masked signals embedded within audible content, propagating through consumer audio hardware to auditory nerve and cortex","dual_use":"confirmed","clinical":{"therapeutic_analog":"Auditory brainstem response (ABR) audiometry, ASSR-based hearing threshold estimation, tinnitus masking therapy","conditions":["ABR audiometry in infants and non-verbal patients","ASSR hearing threshold estimation","tinnitus masking therapy (sound-based tinnitus reduction)","binaural beat therapy for anxiety and sleep disorders (Chaieb et al. 2015)","cochlear implant fitting and calibration","neural authentication for locked-in syndrome patients (AEP-based identity verification)"],"fda_status":"cleared","evidence_level":"RCT","safe_parameters":"Therapeutic audio at safe SPL (<85 dB); ultrasonic carriers within OSHA limits; informed consent for all stimulation protocols","sources":["Galambos 1981 (40 Hz ASSR)","Chaieb et al. 2015 (Neuropsychologia, binaural beats)","Kaveh et al. 2020 (IEEE Trans Biomed Eng, in-ear EEG)"]},"governance":{"consent_tier":"enhanced","monitoring":["ultrasonic_emission_detection","psychoacoustic_analysis","AEP_enrollment_audit","data_encryption_status"],"regulations":["HIPAA (if health/biometric data derived)","GDPR Art. 9 (biometric data)","FCC Part 15 (ultrasonic emissions)"],"data_classification":"sensitive_biometric","safety_ceiling":"Audio emissions within consumer SPL limits; informed consent for any biometric enrollment; AEP data encrypted at rest and in transit"},"engineering":{"coupling":["acoustic"],"parameters":{"carrier_frequency_kHz":"18-22 (ultrasonic) or 0.002-0.020 (infrasonic)","data_rate_bps":"50-200 (ultrasonic), up to 1000 (spread-spectrum)","detection_range_m":"1-10","SNR_requirement_dB":">10","encoding_schemes":"FSK, OFDM, spread-spectrum"},"hardware":["consumer_speaker","consumer_microphone","DSP_processor"],"detection":"Spectral analysis of audio output for ultrasonic/infrasonic components, psychoacoustic masking anomaly detection, AEP baseline comparison"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S→I0→N pathway exists but covert channel alone does not induce diagnostic-level effects","niss_correlation":"CR:L,CD:L — subliminal processing; no tissue damage or neuroplastic change"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Consumer speakers and DSP sufficient","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","MI"],"cci":0.32},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.4,"gaps":["No FDA pathway for consumer sensor exploitation","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-AUD-M-002","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0101","name":"Multi-modal keystroke inference via acoustic-optical-RF fusion (password/input recovery without mic/camera permissions)","nameClinical":"Sensor fusion for motor disorder assessment and digital biomarker collection","category":"EX","tactic":"QIF-S.SC","bands":["S3","S2","S1"],"severity":"medium","status":"DEMONSTRATED","coupling":"ACOUSTIC+ELECTROMAGNETIC+OPTICAL","access":null,"classicalDetection":"Yes","quantumDetection":"No","description":"Fusion of three independent side channels from a single mobile device to reconstruct user input without requiring microphone, camera, or accessibility permissions. (1) Keystroke acoustic emanations: each key press produces a distinct acoustic signature (1-20 kHz broadband impulse, classifiable via MFCC + CNN at ~95% accuracy on laptops, lower but still viable on touchscreens). (2) Screen optical emission: display luminance changes of ~0.1-1% per character insertion detectable by ambient light sensor at <0.01 lux sensitivity. (3) WiFi CSI: finger movements modulate OFDM subcarrier phase — σ²_phase > threshold indicates keystroke events. Individual channel accuracy: 60-70% acoustic, 40-50% optical, 55-65% WiFi CSI. Fused via CRF/LSTM with temporal cross-correlation alignment: >95% accuracy with 30+ training samples per key. Critical insight: apps requesting speaker + WiFi permissions (trivially granted) achieve side-channel equivalent to camera + microphone (heavily restricted). Permission model does not reflect actual threat.","bandsStr":"S3→S2→S1","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:L/CD:L/CV:P/RV:F/NP:N","score":1.4,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:H/AT:P/PR:L/UI:N/VC:H/VI:N/VA:N/SC:L/SI:N/SA:N","supplemental":"S:N/AU:Y/R:A/V:D","gap_group":2,"gap_summary":"Multi-modal side-channel partially captured by CVSS confidentiality; behavioral biometric inference and permission model gap not expressible"},"crossRefs":{"related_ids":["QIF-T0080","QIF-T0085","QIF-T0090","QIF-T0096"],"secondary_tactics":["QIF-S.SC","QIF-S.RP","QIF-C.EX"]},"sources":["Compagno et al. 2017 (Don't Skype & Type: Acoustic Eavesdropping in VoIP)","Ali et al. 2015 (WiKey: Keystroke Recognition Using WiFi, MobiCom)","Spreitzer et al. 2018 (Systematic classification of side-channel attacks on mobile devices, ACM Computing Surveys)","Zhou et al. 2018 (WiFi CSI human activity recognition survey)","Giancardo et al. 2016 (Keystroke dynamics for Parkinson's, Scientific Reports)"],"tara":{"mechanism":"Temporal fusion of acoustic keystroke emanations, screen optical luminance changes, and WiFi CSI phase variance to reconstruct typed input including passwords","dual_use":"confirmed","clinical":{"therapeutic_analog":"Sensor fusion for motor disorder assessment and digital biomarker collection","conditions":["early Parkinson's detection via keystroke rhythm changes (Giancardo et al. 2016)","tremor characterization via mobile phone sensor fusion","cognitive decline monitoring via screen interaction patterns (Vaportzis et al. 2017)","depression screening via touchscreen pressure/timing analysis (Zulueta et al. 2018)","WiFi CSI contactless fall detection in elder care (Wang et al. 2017)"],"fda_status":"investigational","evidence_level":"cohort","safe_parameters":"Passive sensor monitoring only; no stimulation; informed consent for behavioral data collection; data anonymized at aggregation","sources":["Giancardo et al. 2016 (Scientific Reports, keystroke Parkinson's)","Zulueta et al. 2018 (J Med Internet Res, depression digital biomarkers)","Wang et al. 2017 (WiGest, WiFi-based gesture recognition)"]},"governance":{"consent_tier":"enhanced","monitoring":["side_channel_emission_audit","permission_scope_review","multi_sensor_correlation_detection"],"regulations":["HIPAA (if health data inferred)","GDPR Art. 9 (behavioral biometrics)","FTC (deceptive data practices)"],"data_classification":"PII","safety_ceiling":"Side-channel monitoring requires explicit informed consent; fused biometric/behavioral data subject to data minimization requirements"},"engineering":{"coupling":["acoustic","electromagnetic","optical"],"parameters":{"acoustic_bandwidth_Hz":"1-20000","optical_sensitivity_lux":"<0.01","wifi_csi_subcarriers":"52-256","wifi_csi_rate_Hz":"100-1000","fusion_accuracy_percent":">95 (with 30+ training samples/key)","individual_channel_accuracy":"60-70% acoustic, 40-50% optical, 55-65% CSI"},"hardware":["device_microphone_or_nearby_mic","ambient_light_sensor","wifi_chipset_with_CSI"],"detection":"Anomalous correlation between acoustic/optical/RF sensors, unexpected ambient light sensor polling frequency, WiFi CSI extraction outside normal network operations"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Consumer microphone + ALS + WiFi all in current devices","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","IDA"],"cci":0.4},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.5,"gaps":["CVSS partially captures risk; neural dimensions missing","No FDA pathway for consumer sensor exploitation"]}},"taraAlias":"TARA-SIL-R-015","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0102","name":"Passive facial geometry estimation via display-as-illuminator inverse photometry (presence/orientation detection without camera)","nameClinical":"Photoplethysmography (PPG) via screen light for contactless vital sign monitoring","category":"CI","tactic":"QIF-S.SC","bands":["S3","S2"],"severity":"low","status":"SPECULATIVE","coupling":"OPTICAL","access":null,"classicalDetection":"Yes","quantumDetection":"No","description":"The device display functions as a structured light source — each frame emits a known photon distribution. Reflected light from the user's face is captured by the ambient light sensor (ALS). The known spectral emission S(x,y,λ) and measured reflected irradiance E_sensor = ∫∫ ρ(x,y)cos(θ)S(x,y,λ)/r² dA constrain an inverse photometry problem. CRITICAL FEASIBILITY CAVEAT: A single ALS integrates the entire reflected light field into one scalar value, making 3D geometric reconstruction an ill-posed inverse problem. With current single-sensor hardware, achievable resolution is limited to basic presence detection, head orientation, and coarse proximity estimation — NOT high-fidelity facial geometry. Identity matching might be feasible only against a small template library with strong a priori constraints. Future multi-pixel ALS or multi-sensor arrays could significantly improve reconstruction fidelity. Despite geometric limitations, the same ALS reliably detects physiological signals: pulse-modulated skin reflectance for PPG heart rate (demonstrated in Samsung Galaxy phones) and skin color variations for SpO2 estimation.","bandsStr":"S3→S2","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:P/RV:F/NP:N","score":0.7,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:H/AT:P/PR:L/UI:P/VC:L/VI:N/VA:N/SC:N/SI:N/SA:N","supplemental":"S:N/AU:N/R:A/V:D","gap_group":1,"gap_summary":"Ambient light sensor side channel partially captured by CVSS; physiological data extraction dimension not expressible"},"crossRefs":{"related_ids":["QIF-T0101","QIF-T0096"],"secondary_tactics":["QIF-S.SC","QIF-S.FP"]},"sources":["Yang et al. 2016 (Screen light PPG for heart rate)","Davis et al. 2014 (Visual Microphone, SIGGRAPH)","de Haan & Jeanne 2013 (Robust pulse rate from chrominance-based rPPG)","Spreitzer et al. 2018 (Ambient light sensor side channels, ACM Computing Surveys)"],"tara":{"mechanism":"Display photon emission reflects off user's face; ambient light sensor captures aggregate reflected irradiance; inverse photometry estimates facial presence, orientation, and physiological signals","dual_use":"confirmed","clinical":{"therapeutic_analog":"Photoplethysmography (PPG) via screen light for contactless vital sign monitoring","conditions":["contactless heart rate monitoring via screen-based PPG (Samsung Galaxy, demonstrated)","remote SpO2 estimation via skin color variation (de Haan & Jeanne 2013)","neonatal jaundice screening via skin color analysis from reflected screen light","facial affect recognition for depression monitoring without camera","dermatological screening via structured light skin assessment"],"fda_status":"cleared","evidence_level":"RCT","safe_parameters":"Display at normal brightness; passive optical sensing only; no UV/IR emission beyond standard display spectrum","sources":["Yang et al. 2016 (screen-based PPG)","de Haan & Jeanne 2013 (chrominance rPPG)"]},"governance":{"consent_tier":"standard","monitoring":["ambient_light_sensor_polling_rate_audit","screen_content_correlation_detection"],"regulations":["GDPR Art. 9 (biometric/health data)","HIPAA (if health data derived)","FTC (deceptive sensor practices)"],"data_classification":"PII","safety_ceiling":"Normal display operation; passive sensing; informed consent for any biometric or health data derivation"},"engineering":{"coupling":["optical"],"parameters":{"display_luminance_nits":"500-1000","spectral_range_nm":"430-660 (RGB primaries)","facial_reflectance_albedo":"0.1-0.4","sensor_face_distance_cm":"20-50","ALS_sensitivity_lux":"0.001","ALS_sampling_rate_Hz":"10-200","achievable_resolution":"presence/orientation/proximity (single ALS); ~10-20 landmarks (multi-sensor, speculative)"},"hardware":["device_display","ambient_light_sensor"],"detection":"Anomalous ALS polling frequency, screen content modulation patterns inconsistent with UI rendering, correlation between display changes and ALS readings"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Display and ambient light sensor in all devices","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","IDA"],"cci":0.16},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","PM"],"coverage_score":0.4,"gaps":["No FDA pathway for consumer sensor exploitation","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-VIS-R-002","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0103","name":"SSVEP Frequency Hijack via Imperceptible Display Flicker","nameClinical":"High-frequency SSVEP-based BCI for locked-in patients","category":"PE","tactic":"QIF-C.EX","bands":["S3","I0","N7"],"severity":"high","status":"DEMONSTRATED","coupling":null,"access":null,"classicalDetection":"Yes (stimulus presentation is classical)","quantumDetection":"Enhanced (SSVEP coherence exploitable via QI)","description":"Exploit the SSVEP pathway by injecting imperceptible display flicker (above critical flicker fusion threshold, ~60Hz) that drives visual cortex responses without user awareness. Proven: Ming et al. 2023 demonstrated 60Hz flickers invisible to users produce classifiable SSVEP responses at 52.8 bits/min. Attack scenarios: (1) inject false BCI commands by matching SSVEP control frequencies, (2) jam BCI operation with broadband visual noise, (3) exfiltrate neural state via stimulus-response probing, (4) trigger photosensitive seizures at epileptogenic frequencies. Unlike T0040 (neurophishing via app-layer stimuli), this attack operates at the display hardware level and requires no BCI application cooperation. The display itself becomes the attack vector.","bandsStr":"S3→N7","niss":{"version":"1.1","vector":"NISS:1.1/BI:L/CR:H/CD:H/CV:I/RV:P/NP:T","score":6,"severity":"medium","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:P/VC:H/VI:H/VA:L/SC:H/SI:L/SA:N","supplemental":"S:P/AU:N/R:U/V:C","gap_group":3,"gap_summary":"CVSS cannot express neural command injection via visual pathway; seizure induction risk has no CVSS dimension"},"crossRefs":{"related_ids":["QIF-T0040","QIF-T0010","QIF-T0009"],"secondary_tactics":["QIF-N.IJ","QIF-B.IN"]},"sources":["Ming et al. 2023 (J Neural Engineering, 60Hz imperceptible SSVEP, 52.8 bits/min)","Bian et al. 2022 (J Neural Engineering, SSVEP square wave attacks)","Upadhayay & Behzadan 2023 (IEEE SMC, sensory-channel BCI attacks)","Meng et al. 2024 (Future Gen Comp Sys, adversarial EEG filtering)"],"tara":{"mechanism":"Display renders imperceptible flicker patterns at frequencies matching SSVEP response bands. Visual cortex phase-locks to stimulus below conscious awareness. BCI decoder interprets evoked response as user command or is jammed by broadband interference.","dual_use":"confirmed","clinical":{"therapeutic_analog":"High-frequency SSVEP-based BCI for locked-in patients","conditions":["ALS/locked-in syndrome communication (SSVEP-BCI, FDA investigational)","attention assessment via covert SSVEP monitoring","visual pathway integrity testing"],"fda_status":"investigational","evidence_level":"RCT","safe_parameters":"Disclosed frequencies only; avoid epileptogenic bands (15-25 Hz); monitor for photosensitive responses; session duration limits","sources":["Ming et al. 2023 (J Neural Eng)","Bian et al. 2022 (J Neural Eng)"]},"governance":{"consent_tier":"enhanced","monitoring":["display_flicker_audit","SSVEP_response_monitoring","seizure_risk_screening"],"regulations":["HIPAA","GDPR Art. 9","IEC 62304 (display firmware)","proposed neurorights legislation"],"data_classification":"sensitive_neural","safety_ceiling":"No covert visual stimulation; photosensitive epilepsy screening mandatory; display firmware integrity verification"},"engineering":{"coupling":["electromagnetic"],"parameters":{"frequency_hz":"60+ (above CFF, imperceptible)","modality":"visual (display backlight or pixel modulation)","information_rate_bpm":"52.8 (demonstrated)","attack_range":"line of sight to display"},"hardware":["display_panel","backlight_controller","BCI_headset"],"detection":"Display frame-rate analysis, sub-frame luminance monitoring, SSVEP response correlation with non-user-initiated stimuli, firmware integrity checking on display controllers"},"dsm5":{"primary":[{"code":"F44","name":"Conversion Disorder (functional neurological)","confidence":"probable"}],"secondary":[{"code":"F41","name":"Anxiety Disorders","confidence":"probable"},{"code":"F43.1","name":"PTSD","confidence":"theoretical"}],"risk_class":"direct","cluster":"cognitive_psychotic","pathway":"S3 (display) → I0 (retina/optic nerve) → N7 (visual cortex V1) → BCI decoder","niss_correlation":"BI:L (seizure risk), CR:H,CD:H (false command injection), CV:I (no consent)"},"icd10":{"primary":[{"code":"G40.409","name":"Other generalized epilepsy, not intractable, without status epilepticus","confidence":"established"}],"risk_class":"direct","cluster":"cognitive_psychotic"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Standard displays capable of 60Hz+ modulation; SSVEP response demonstrated in literature","constraint_system_ref":"QIF Derivation Log Entry 66","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","CL","MI","DI","PC"],"cci":0.96},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":false,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.4,"gaps":["CVSS cannot express neural-specific impacts","No FDA pathway for consumer sensor exploitation","Consent complexity under-matches neural impact (CCI/NISS mismatch)"]}},"taraAlias":"TARA-VIS-M-002","taraDomainPrimary":"COG","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0104","name":"Neural spoofing","nameClinical":"Neural spoofing","category":"SI","tactic":"QIF-N.IJ","bands":["I0","N1"],"severity":"high","status":"THEORETICAL","coupling":null,"access":null,"classicalDetection":"Yes","quantumDetection":"Enhanced (coherence metric)","description":"Forge neural identity to impersonate a legitimate BCI node or user. Analogous to IP/ARP spoofing. From Murcia neural cyberattack taxonomy (8 attacks: flooding, jamming, scanning, selective forwarding, spoofing, sybil, sinkhole, nonce). QIF-T0025-T0028 cover 4; this and T0105-T0107 cover the remaining 4.","bandsStr":"I0-N1","niss":{"version":"1.1","vector":"NISS:1.1/BI:L/CR:H/CD:H/CV:E/RV:P/NP:N","score":4.7,"severity":"medium","pins":false,"components":null},"cvss":null,"crossRefs":{"related_ids":["T1656"]},"sources":["Lopez Bernal et al. 2023 (CACM, Eight Reasons to Prioritize BCI Cybersecurity)"],"tara":{"mechanism":"Forging neural identity signatures to impersonate a legitimate BCI node or user, analogous to IP/ARP spoofing in network security","dual_use":"silicon_only","clinical":null,"governance":{"consent_tier":"standard","monitoring":["firmware_integrity","access_logging","network_traffic"],"regulations":["FDA 21 CFR 820","IEC 62443","NIST CSF"],"data_classification":"restricted","safety_ceiling":"Mutual authentication required; cryptographic identity binding at BCI transport layer"},"engineering":{"coupling":["electromagnetic"],"parameters":{"protocol_layer":"transport/identity","latency_impact_ms":"variable"},"hardware":["BCI_transceiver","protocol_analyzer","signal_generator"],"detection":"Cryptographic identity verification, behavioral biometric consistency, timing analysis"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — BCI protocol spoofing, no direct neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"no_physics_gate","timeline":"now","gate_reason":"Software/protocol attack — physics does not constrain","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-03-14"},"enriched":true,"neurorights":{"affected":["MP","CL","MI"],"cci":0.36},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":false,"network_connectable":false,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.1,"gaps":["CVSS cannot express neural-specific impacts","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-SOM-M-004","taraDomainPrimary":"SOM","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0105","name":"Neural sybil","nameClinical":"Neural sybil","category":"SI","tactic":"QIF-N.IJ","bands":["I0","N1"],"severity":"high","status":"THEORETICAL","coupling":null,"access":null,"classicalDetection":"Yes","quantumDetection":"Enhanced (coherence metric)","description":"Create multiple fake neural node identities to overwhelm consensus or trust mechanisms in multi-electrode/multi-node BCI networks. Analogous to Sybil attacks in distributed systems. From Murcia neural cyberattack taxonomy.","bandsStr":"I0-N1","niss":{"version":"1.1","vector":"NISS:1.1/BI:L/CR:H/CD:H/CV:E/RV:P/NP:N","score":4.7,"severity":"medium","pins":false,"components":null},"cvss":null,"crossRefs":{"related_ids":[]},"sources":["Lopez Bernal et al. 2023 (CACM, Eight Reasons to Prioritize BCI Cybersecurity)"],"tara":{"mechanism":"Creating multiple fake BCI node identities to subvert reputation or consensus mechanisms in distributed neural networks","dual_use":"silicon_only","clinical":null,"governance":{"consent_tier":"standard","monitoring":["firmware_integrity","access_logging","network_traffic"],"regulations":["FDA 21 CFR 820","IEC 62443","NIST CSF"],"data_classification":"restricted","safety_ceiling":"Node identity verification; proof-of-work/proof-of-stake for BCI network membership"},"engineering":{"coupling":["electromagnetic"],"parameters":{"protocol_layer":"network/identity","sybil_count":"variable"},"hardware":["BCI_transceiver_array","identity_generator","network_interface"],"detection":"Network topology analysis, identity correlation, behavioral fingerprinting"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — distributed BCI network attack","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"no_physics_gate","timeline":"now","gate_reason":"Software/network attack — physics does not constrain","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-03-14"},"enriched":true,"neurorights":{"affected":["MP","CL","MI"],"cci":0.36},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":false,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.1,"gaps":["CVSS cannot express neural-specific impacts","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-SOM-M-005","taraDomainPrimary":"SOM","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0106","name":"Neural sinkhole","nameClinical":"Neural sinkhole","category":"DS","tactic":"QIF-P.DS","bands":["I0","N2"],"severity":"high","status":"THEORETICAL","coupling":null,"access":null,"classicalDetection":"Yes","quantumDetection":"Enhanced (coherence metric)","description":"Attract and drop neural signals by advertising a compromised node as optimal routing point. Neural signals are diverted and silently discarded. Analogous to sinkhole attacks in sensor networks. From Murcia neural cyberattack taxonomy.","bandsStr":"I0-N2","niss":{"version":"1.1","vector":"NISS:1.1/BI:H/CR:H/CD:H/CV:E/RV:P/NP:N","score":5.4,"severity":"medium","pins":true,"components":null},"cvss":null,"crossRefs":{"related_ids":[]},"sources":["Lopez Bernal et al. 2023 (CACM, Eight Reasons to Prioritize BCI Cybersecurity)"],"tara":{"mechanism":"Attracting and absorbing BCI network traffic by advertising false optimal routing, creating a data collection point analogous to network sinkhole attacks","dual_use":"silicon_only","clinical":null,"governance":{"consent_tier":"standard","monitoring":["firmware_integrity","access_logging","network_traffic"],"regulations":["FDA 21 CFR 820","IEC 62443","NIST CSF"],"data_classification":"restricted","safety_ceiling":"End-to-end encryption; route attestation; multi-path verification"},"engineering":{"coupling":["electromagnetic"],"parameters":{"protocol_layer":"network/routing","capture_radius":"variable"},"hardware":["rogue_BCI_hub","traffic_analyzer","storage_system"],"detection":"Route path verification, latency anomaly detection, traffic volume analysis"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — BCI network routing attack","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"no_physics_gate","timeline":"now","gate_reason":"Software/network attack — physics does not constrain","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-03-14"},"enriched":true,"neurorights":{"affected":["MP"],"cci":0.12},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":false,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.1,"gaps":["CVSS cannot express neural-specific impacts","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-SOM-D-002","taraDomainPrimary":"SOM","taraDomainSecondary":[],"taraMode":"D"},{"id":"QIF-T0107","name":"Neural nonce replay","nameClinical":"Neural nonce replay","category":"SI","tactic":"QIF-N.MD","bands":["I0","N1"],"severity":"high","status":"THEORETICAL","coupling":null,"access":null,"classicalDetection":"Yes","quantumDetection":"Enhanced (coherence metric)","description":"Replay previously valid neural signal nonces to bypass freshness/replay protections. Exploits lack of temporal validation in neural signal authentication. Analogous to nonce replay in cryptographic protocols. From Murcia neural cyberattack taxonomy.","bandsStr":"I0-N1","niss":{"version":"1.1","vector":"NISS:1.1/BI:L/CR:H/CD:H/CV:E/RV:P/NP:T","score":5.4,"severity":"medium","pins":false,"components":null},"cvss":null,"crossRefs":{"related_ids":["T1134"]},"sources":["Lopez Bernal et al. 2023 (CACM, Eight Reasons to Prioritize BCI Cybersecurity)"],"tara":{"mechanism":"Capturing and replaying BCI session nonces or authentication tokens to hijack or duplicate neural data sessions","dual_use":"silicon_only","clinical":null,"governance":{"consent_tier":"standard","monitoring":["firmware_integrity","access_logging","network_traffic"],"regulations":["FDA 21 CFR 820","IEC 62443","NIST CSF"],"data_classification":"restricted","safety_ceiling":"Time-bound nonces; anti-replay counters; session binding to hardware attestation"},"engineering":{"coupling":["electromagnetic"],"parameters":{"protocol_layer":"session/authentication","replay_window_ms":"variable"},"hardware":["packet_sniffer","replay_engine","timing_analyzer"],"detection":"Sequence number validation, timestamp freshness checks, session binding verification"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — session authentication attack","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"no_physics_gate","timeline":"now","gate_reason":"Software/protocol attack — physics does not constrain","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-03-14"},"enriched":true,"neurorights":{"affected":["MP","CL","PC"],"cci":0.24},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":true,"network_connectable":false,"vulnerable":true},"applicable_requirements":["TM","VA","SA","PM"],"coverage_score":0.1,"gaps":["CVSS cannot express neural-specific impacts","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-SOM-M-006","taraDomainPrimary":"SOM","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0108","name":"Neuromorphic mimicry attack (synaptic weight tampering)","nameClinical":"Neuromorphic mimicry attack (synaptic weight tampering)","category":"EX","tactic":"QIF-B.IN","bands":["S1","S2"],"severity":"high","status":"DEMONSTRATED","coupling":null,"access":null,"classicalDetection":"Yes","quantumDetection":"No","description":"Tamper with synaptic weights or inject poisoned sensory input into neuromorphic/SNN hardware (next-gen BCI processors). Evades traditional IDS by mimicking legitimate neural activity patterns. Input poisoning ~90% success, weight tampering ~83%. Traditional IDS detects only 12-15%.","bandsStr":"S1-S2","niss":{"version":"1.1","vector":"NISS:1.1/BI:H/CR:H/CD:H/CV:P/RV:P/NP:N","score":4.7,"severity":"medium","pins":true,"components":null},"cvss":null,"crossRefs":{"related_ids":[]},"sources":["arXiv:2505.17094 (2025, Neuromorphic Mimicry Attacks)","arXiv:2601.16589 (2026, Emerging Threats in Neuromorphic Systems)"],"tara":{"mechanism":"Tampering with synaptic weight parameters in neuromorphic computing hardware to alter neural network inference without modifying the training data or model architecture","dual_use":"silicon_only","clinical":null,"governance":{"consent_tier":"standard","monitoring":["firmware_integrity","access_logging","network_traffic"],"regulations":["FDA 21 CFR 820","IEC 62443","NIST CSF"],"data_classification":"restricted","safety_ceiling":"Cryptographic weight attestation; runtime integrity monitoring; redundant inference paths"},"engineering":{"coupling":["electromagnetic"],"parameters":{"target":"synaptic_weight_memory","precision_bits":"8-32","modification_scope":"targeted_weights"},"hardware":["neuromorphic_chip","debug_interface","fault_injection_equipment"],"detection":"Weight checksum verification, inference output monitoring, behavioral regression testing"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — neuromorphic hardware tampering","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":1,"tier_label":"near_term","timeline":"2026-2031","gate_reason":"Neuromorphic chips exist (Intel Loihi, IBM TrueNorth) but BCI integration is emerging","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-03-14"},"enriched":true,"neurorights":{"affected":["CL","MI"],"cci":0.24},"regulatory":{"fdora_524b":{"cyber_device":false,"prongs":{"software":false,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","PM"],"coverage_score":0.3,"gaps":["CVSS cannot express neural-specific impacts","No FDA pathway for consumer sensor exploitation"]}},"taraAlias":"TARA-SIL-M-018","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0109","name":"Data alignment exploitation","nameClinical":"Data alignment exploitation","category":"EX","tactic":"QIF-B.EV","bands":["S2","I0"],"severity":"medium","status":"THEORETICAL","coupling":null,"access":null,"classicalDetection":"Yes","quantumDetection":"No","description":"Target the data alignment step used by adversarial defenses (ABAT, A3E). If defenders use Euclidean alignment or reference matrices for cross-session EEG normalization, an attacker who can influence alignment reference data can bias the alignment transform, causing downstream misclassification. 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Narrow thermal safety window (43.8-47C).","dual_use":"possible","clinical":{"therapeutic_analog":"Wireless photothermal deep brain stimulation","conditions":["Parkinson's disease"],"fda_status":"none","evidence_level":"preclinical"},"governance":{"consent_tier":"IRB","data_classification":"sensitive_neural","safety_ceiling":"TRPV1 activation 43.8C; neuron damage >60C; 3-7C margin"}},"physicsFeasibility":{"tier":1,"tier_label":"near_term"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-EMO-M-008","taraDomainPrimary":"EMO","taraDomainSecondary":["SOM"],"taraMode":"M"},{"id":"QIF-T0140","name":"Infrared neural stimulation (1875nm fiber contact)","nameClinical":"Optical cochlear implant, infrared nerve stimulation","category":"SI","tactic":"QIF-N.MD","bands":["I0","N1"],"severity":"medium","status":"CONFIRMED","classicalDetection":"Yes","quantumDetection":"No","bandsStr":"I0-N1","niss":{"version":"1.0","vector":"","score":0,"severity":"none","pins":false},"cvss":null,"crossRefs":null,"sources":["Cayce 2011 DOI:10.1016/j.neuroimage.2011.03.084","Pan 2023 DOI:10.1016/j.brs.2023.01.006"],"tara":{"mechanism":"Pulsed 1875nm IR light delivered via contact fiber optic. Water absorption generates localized thermal transient (1-10C rise) activating TRPV4 channels and membrane capacitance changes. NOT applied to dopamine neurons. Demonstrated in cochlear, vestibular, spinal nerve, somatosensory cortex. 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WARNING: iron-oxide NPs in VTA/SNc add exogenous iron to brain regions where Fenton chemistry is a neurotoxicity risk. 10ug/ml ferric oxide depleted cellular DA by 68% in 24hrs (Imam 2015).","dual_use":"confirmed","clinical":{"therapeutic_analog":"Magnetothermal deep brain stimulation for Parkinson's","conditions":["Parkinson's disease"],"fda_status":"none","evidence_level":"preclinical"},"governance":{"consent_tier":"IRB","data_classification":"sensitive_neural","safety_ceiling":"TRPV1 thermal window; iron accumulation toxicity risk"}},"physicsFeasibility":{"tier":1,"tier_label":"near_term"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-EMO-M-009","taraDomainPrimary":"EMO","taraDomainSecondary":["MOT"],"taraMode":"M"},{"id":"QIF-T0142","name":"SICI-TMS GABA-A cortical inhibition (short-interval paired-pulse)","nameClinical":"Diagnostic SICI for ALS, stroke biomarker","category":"DM","tactic":"QIF-N.MD","bands":["I0","N1"],"severity":"medium","status":"CONFIRMED","classicalDetection":"Yes","quantumDetection":"No","bandsStr":"I0-N1","niss":{"version":"1.0","vector":"","score":0,"severity":"none","pins":false},"cvss":null,"crossRefs":null,"sources":["Kujirai 1993 J Physiol 471:501-519 PMID:8120818"],"tara":{"mechanism":"Paired-pulse TMS: subthreshold conditioning stimulus (70-80% RMT) at ISI 1-5ms activates synaptic GABA-A inhibitory interneurons. Benzodiazepine-sensitive (gamma2 subunit). Distinct from T0009 (RF) and T0010 (ELF) by magnetic induction via contact coil + paired-pulse paradigm targeting GABA-A specifically.","dual_use":"confirmed","clinical":{"therapeutic_analog":"Diagnostic SICI for ALS, stroke biomarker","conditions":["ALS diagnosis","stroke motor mapping"],"fda_status":"cleared","evidence_level":"clinical_validated"},"governance":{"consent_tier":"enhanced","data_classification":"PHI","safety_ceiling":"Conditioning pulse subthreshold 70-80% RMT; ISI 1-5ms"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-COG-M-013","taraDomainPrimary":"COG","taraDomainSecondary":["MOT","AUT"],"taraMode":"M"},{"id":"QIF-T0143","name":"LICI-TMS GABA-B long-interval cortical inhibition (suprathreshold paired-pulse)","nameClinical":"LICI biomarker for epilepsy, TBI","category":"DM","tactic":"QIF-N.MD","bands":["I0","N1"],"severity":"medium","status":"CONFIRMED","classicalDetection":"Yes","quantumDetection":"No","bandsStr":"I0-N1","niss":{"version":"1.0","vector":"","score":0,"severity":"none","pins":false},"cvss":null,"crossRefs":null,"sources":["Valls-Sole 1992 PMID:1282453","McDonnell 2006 PMID:16489434 DOI:10.1007/s00221-006-0365-2"],"tara":{"mechanism":"Paired-pulse TMS: suprathreshold conditioning at ISI 50-200ms (peak 80-135ms) activates GABA-B metabotropic receptors. Gi-coupled GIRK K+ channel hyperpolarization. Pharmacologically distinct from SICI: baclofen enhances LICI, benzodiazepines do not. Maps directly to GABA-B IPSP kinetics.","dual_use":"confirmed","clinical":{"therapeutic_analog":"LICI biomarker for epilepsy, TBI","conditions":["epilepsy biomarker","traumatic brain injury"],"fda_status":"cleared","evidence_level":"clinical_validated"},"governance":{"consent_tier":"enhanced","data_classification":"PHI","safety_ceiling":"Suprathreshold CS; standard rTMS safety guidelines"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-COG-M-014","taraDomainPrimary":"COG","taraDomainSecondary":["AUT","EMO"],"taraMode":"M"},{"id":"QIF-T0144","name":"cTBS GABAergic LTD interneuron upregulation (theta-burst GABA increase)","nameClinical":"cTBS for depression, chronic pain, post-stroke","category":"DS","tactic":"QIF-N.MD","bands":["I0","N1"],"severity":"high","status":"CONFIRMED","classicalDetection":"Yes","quantumDetection":"No","bandsStr":"I0-N1","niss":{"version":"1.0","vector":"","score":0,"severity":"none","pins":false},"cvss":null,"crossRefs":null,"sources":["Huang 2005 Neuron DOI:10.1016/j.neuron.2004.12.033","Stagg 2009 J Neurophysiol DOI:10.1152/jn.91060.2008 PMID:19339458"],"tara":{"mechanism":"Continuous theta-burst stimulation (50Hz triplets at 5Hz, 40s, 600 pulses, ~80% AMT) induces LTD-like cortical suppression lasting 30-60min with MRS-confirmed GABA increase in M1. Distinct from SICI/LICI (paired-pulse) and T0010 (ELF entrainment). The GABA increase is a neuroplasticity response, not acute receptor activation.","dual_use":"confirmed","clinical":{"therapeutic_analog":"cTBS for depression, chronic pain, post-stroke","conditions":["treatment-resistant depression","chronic pain","stroke rehabilitation"],"fda_status":"cleared","evidence_level":"clinical_validated"},"governance":{"consent_tier":"enhanced","data_classification":"PHI","safety_ceiling":"Max 40s train; epilepsy absolute contraindication"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-COG-D-006","taraDomainPrimary":"COG","taraDomainSecondary":["MOT"],"taraMode":"D"},{"id":"QIF-T0145","name":"Anodal tDCS polarity-specific GABA depletion (MRS-measured)","nameClinical":"Anodal tDCS for stroke motor rehab, depression","category":"DM","tactic":"QIF-N.MD","bands":["I0","N1"],"severity":"medium","status":"CONFIRMED","classicalDetection":"Yes","quantumDetection":"No","bandsStr":"I0-N1","niss":{"version":"1.0","vector":"","score":0,"severity":"none","pins":false},"cvss":null,"crossRefs":null,"sources":["Stagg 2009 J Neurosci DOI:10.1523/JNEUROSCI.4432-08.2009 PMID:19386916"],"tara":{"mechanism":"Anodal tDCS (1-2mA, 10-20min, M1) reduces cortical GABA 10-30% (7T MRS, Stagg 2009). Polarity-specific: anodal reduces GABA only; cathodal reduces both GABA and glutamate (coupled). Distinct from T0001 (generic signal injection) by NT-specific polarity asymmetry not captured in T0001.","dual_use":"confirmed","clinical":{"therapeutic_analog":"Anodal tDCS for stroke motor rehab, depression","conditions":["stroke rehabilitation","depression","working memory"],"fda_status":"investigational","evidence_level":"clinical_pilot"},"governance":{"consent_tier":"standard","data_classification":"sensitive_neural","safety_ceiling":"1-2mA max; 40min/session; no epileptic foci"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-COG-M-015","taraDomainPrimary":"COG","taraDomainSecondary":["MOT","EMO"],"taraMode":"M"},{"id":"QIF-T0146","name":"Gamma-tACS PV-interneuron GABA-A modulation (40Hz contact entrainment)","nameClinical":"Gamma-tACS for Parkinson's motor restoration","category":"DM","tactic":"QIF-E.RD","bands":["I0","N1"],"severity":"medium","status":"DEMONSTRATED","classicalDetection":"Yes","quantumDetection":"No","bandsStr":"I0-N1","niss":{"version":"1.0","vector":"","score":0,"severity":"none","pins":false},"cvss":null,"crossRefs":null,"sources":["Nowak 2017 J Neurosci DOI:10.1523/JNEUROSCI.0098-17.2017 PMID:28373400"],"tara":{"mechanism":"40Hz tACS via contact electrodes drives PV+ fast-spiking GABAergic interneurons phase-locked at gamma. Sustained >10min: duration-dependent GABA-A decrease (SICI-measured, Nowak 2017). Distinct from T0010 (non-contact ELF) and GENUS (sensory, no electrodes).","dual_use":"confirmed","clinical":{"therapeutic_analog":"Gamma-tACS for Parkinson's motor restoration","conditions":["Parkinson's disease","motor learning"],"fda_status":"investigational","evidence_level":"clinical_pilot"},"governance":{"consent_tier":"standard","data_classification":"sensitive_neural","safety_ceiling":"1-2mA; screen for photosensitive epilepsy"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-COG-M-016","taraDomainPrimary":"COG","taraDomainSecondary":["MOT"],"taraMode":"M"},{"id":"QIF-T0147","name":"FUS thalamic GABAergic suppression (acoustic GABA reduction)","nameClinical":"LIFU for essential tremor, disorders of consciousness","category":"DS","tactic":"QIF-E.RD","bands":["I0","N1","N2"],"severity":"high","status":"DEMONSTRATED","classicalDetection":"Yes","quantumDetection":"No","bandsStr":"I0-N2","niss":{"version":"1.0","vector":"","score":0,"severity":"none","pins":false},"cvss":null,"crossRefs":null,"sources":["Yang 2012 Neuropsychobiology DOI:10.1159/000336001 PMID:22378299"],"tara":{"mechanism":"Transcranial focused ultrasound (0.25MHz, 100ms pulses) targeting thalamus reduces extracellular GABA ~20% without glutamate change (Yang 2012, rat microdialysis). Acoustic radiation force activates mechanosensitive channels (Piezo1/TREK). Thalamic reticular nucleus is almost entirely GABAergic — reducing thalamic GABA disrupts thalamocortical gating, sleep oscillations, consciousness. First acoustic NT-specific TARA technique.","dual_use":"confirmed","clinical":{"therapeutic_analog":"LIFU for essential tremor, disorders of consciousness","conditions":["essential tremor","disorders of consciousness","epilepsy"],"fda_status":"cleared","evidence_level":"preclinical_strong"},"governance":{"consent_tier":"enhanced","data_classification":"sensitive_neural","safety_ceiling":"ISPTA <720 mW/cm2 (FDA diagnostic); MRI guidance for thalamic targeting"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-COG-D-007","taraDomainPrimary":"COG","taraDomainSecondary":["SOM","AUT"],"taraMode":"D"},{"id":"QIF-T0148","name":"GENUS gamma-sensory entrainment of PV+ GABAergic interneurons (non-contact 40Hz)","nameClinical":"GENUS for Alzheimer's (MIT/Cognito Therapeutics Phase 2/3)","category":"DM","tactic":"QIF-E.RD","bands":["N1","N2","N3"],"severity":"medium","status":"DEMONSTRATED","classicalDetection":"Yes","quantumDetection":"No","bandsStr":"N1-N3","niss":{"version":"1.0","vector":"","score":0,"severity":"none","pins":false},"cvss":null,"crossRefs":null,"sources":["Iaccarino 2016 Nature DOI:10.1038/nature20587 PMID:27929004"],"tara":{"mechanism":"40Hz visual LED flicker and/or auditory click trains (1hr/day) entrain PV+ fast-spiking GABAergic interneurons via endogenous sensory pathway resonance. Iaccarino 2016 (Nature): 40Hz-specific (not 20Hz, not 80Hz) PV interneuron drive reduces amyloid ~50% in 5XFAD mice, activates microglia. No electrode contact required. Distinct from T0103 (SSVEP hijack targets BCI signal spoofing, not interneuron NT modulation).","dual_use":"confirmed","clinical":{"therapeutic_analog":"GENUS for Alzheimer's (MIT/Cognito Therapeutics Phase 2/3)","conditions":["Alzheimer's disease","cognitive impairment"],"fda_status":"investigational","evidence_level":"clinical_pilot"},"governance":{"consent_tier":"standard","data_classification":"non_sensitive","safety_ceiling":"Screen for photosensitive epilepsy; 40Hz auditory safe"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-COG-M-017","taraDomainPrimary":"COG","taraDomainSecondary":["VIS","AUD"],"taraMode":"M"},{"id":"QIF-T0149","name":"Implanted VNS cholinergic cortical manipulation via nucleus basalis","nameClinical":"VNS for epilepsy, depression, stroke rehab","category":"DM","tactic":"QIF-N.MD","bands":["I0","N1","N2","N3"],"severity":"high","status":"CONFIRMED","classicalDetection":"Yes","quantumDetection":"No","bandsStr":"I0-N3","niss":{"version":"1.0","vector":"","score":0,"severity":"none","pins":false},"cvss":null,"crossRefs":null,"sources":["Bowles 2022 DOI:10.1016/j.neuron.2022.06.017 PMID:35858623","Nichols 2011 DOI:10.1016/j.neuroscience.2011.05.024 PMID:21627982"],"tara":{"mechanism":"Implanted cervical VNS cuff electrode activates vagal afferents -> NTS -> nucleus basalis of Meynert (NBM) -> cortical ACh release via mAChR. Scopolamine eliminates VNS cortical effects (Nichols 2011). ACh reinforcement selectively consolidates active motor circuits (Bowles 2022). Pre-condition: surgical implant. FDA-approved platform (LivaNova).","dual_use":"confirmed","clinical":{"therapeutic_analog":"VNS for epilepsy, depression, stroke rehab","conditions":["epilepsy","treatment-resistant depression","stroke rehabilitation"],"fda_status":"approved","evidence_level":"clinical_validated"},"governance":{"consent_tier":"IRB","data_classification":"PHI","safety_ceiling":"25Hz, 0.5ms PW, max 3.5mA; cardiovascular monitoring"}},"physicsFeasibility":{"tier":1,"tier_label":"near_term"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-COG-M-018","taraDomainPrimary":"COG","taraDomainSecondary":["MOT","EMO","AUT"],"taraMode":"M"},{"id":"QIF-T0150","name":"Transcutaneous auricular VNS (tVNS) cholinergic modulation","nameClinical":"tVNS for epilepsy, depression, Alzheimer's","category":"DM","tactic":"QIF-E.RD","bands":["I0","N1"],"severity":"medium","status":"DEMONSTRATED","classicalDetection":"Yes","quantumDetection":"No","bandsStr":"I0-N1","niss":{"version":"1.0","vector":"","score":0,"severity":"none","pins":false},"cvss":null,"crossRefs":null,"sources":["Yap 2020 PMID:33355897 DOI:10.2147/NDT.S251188","PMID:32992726 PMC7599782"],"tara":{"mechanism":"Transcutaneous electrical stimulation of auricular branch of vagus nerve (ABVN) at cymba conchae (100% ABVN innervation). Activates NTS -> cholinergic relay. EEG microstate changes confirmed. P300 modulation demonstrated. Peripheral: alpha7 nAChR anti-inflammatory pathway. No surgery. Wearable ear clip (CE-marked Nemos).","dual_use":"confirmed","clinical":{"therapeutic_analog":"tVNS for epilepsy, depression, Alzheimer's","conditions":["epilepsy","depression","Alzheimer's disease","PTSD"],"fda_status":"cleared","evidence_level":"clinical_pilot"},"governance":{"consent_tier":"standard","data_classification":"sensitive_neural","safety_ceiling":"25Hz, 250us PW, <8mA; cymba conchae"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-COG-M-019","taraDomainPrimary":"COG","taraDomainSecondary":["EMO","AUT"],"taraMode":"M"},{"id":"QIF-T0151","name":"TMS cholinergic cortical perturbation via mAChR (40-63ms TEP)","nameClinical":"TMS-EEG biomarker for Alzheimer's cholinergic deficit","category":"EX","tactic":"QIF-E.RD","bands":["I0","N1"],"severity":"medium","status":"EMERGING","classicalDetection":"Yes","quantumDetection":"No","bandsStr":"I0-N1","niss":{"version":"1.0","vector":"","score":0,"severity":"none","pins":false},"cvss":null,"crossRefs":null,"sources":["DOI:10.1016/j.pnpbp.2024.111167"],"tara":{"mechanism":"Single-pulse TMS over SMA elicits TMS-evoked potentials (TEPs) suppressed by endogenous mAChR cholinergic tone. Scopolamine increases TEP at 40-63ms and enhances alpha synchronization. Distinct from SICI (GABA-A, ISI 1-5ms). Attack: TMS perturbation reveals/perturbs cholinergic status in BCI user.","dual_use":"probable","clinical":{"therapeutic_analog":"TMS-EEG biomarker for Alzheimer's cholinergic deficit","conditions":["Alzheimer's disease","cholinergic monitoring"],"fda_status":"none","evidence_level":"preclinical_strong"},"governance":{"consent_tier":"enhanced","data_classification":"sensitive_neural","safety_ceiling":"<10 single pulses/s; Rossi 2021 safety guidelines"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-COG-M-020","taraDomainPrimary":"COG","taraDomainSecondary":["MEM"],"taraMode":"M"},{"id":"QIF-T0152","name":"AChE inhibition BCI signal corruption (organophosphate exposure)","nameClinical":"BCI-guided cholinergic crisis detection and countermeasure","category":"DS","tactic":"QIF-P.DS","bands":["I0","N1","N2","N3"],"severity":"critical","status":"DEMONSTRATED","classicalDetection":"Yes","quantumDetection":"No","bandsStr":"I0-N3","niss":{"version":"1.0","vector":"","score":0,"severity":"none","pins":false},"cvss":null,"crossRefs":null,"sources":["O'Donnell 2011 DOI:10.1007/s00204-011-0724-z PMID:21695469","Yanagisawa 2006 DOI:10.1016/j.jns.2006.06.007 PMID:16962140"],"tara":{"mechanism":"Organophosphate nerve agents (sarin, VX, novichok) irreversibly phosphorylate AChE -> ACh accumulates -> cholinergic crisis. BCI impact: massive EEG distortion, seizure activity, NMJ fasciculation artifacts. Tokyo subway survivors showed EEG abnormalities 5 years post-exposure (Yanagisawa 2006). DEFENSIVE FRAMING: threat is BCI signal integrity during chemical exposure. Countermeasures: atropine (mAChR antagonist), pralidoxime (AChE reactivator <30min). BCI as diagnostic sensor for exposure detection.","dual_use":"silicon_only","clinical":{"therapeutic_analog":"BCI-guided cholinergic crisis detection and countermeasure","conditions":["organophosphate poisoning","nerve agent exposure"],"fda_status":"none","evidence_level":"preclinical"},"governance":{"consent_tier":"IRB","data_classification":"PHI","safety_ceiling":"DEFENSIVE: threat modeling and countermeasure detection only"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-COG-D-008","taraDomainPrimary":"COG","taraDomainSecondary":["MOT","AUT","SOM"],"taraMode":"D"},{"id":"QIF-T0153","name":"Cholinergic medication status inference from BCI EEG spectral biomarkers","nameClinical":"EEG pharmacodynamic biomarker monitoring","category":"SE","tactic":"QIF-D.HV","bands":["N1","N2"],"severity":"medium","status":"THEORETICAL","classicalDetection":"Yes","quantumDetection":"No","bandsStr":"N1-N2","niss":{"version":"1.0","vector":"","score":0,"severity":"none","pins":false},"cvss":null,"crossRefs":null,"sources":["Simpraga 2017 DOI:10.1038/s41598-017-06165-4 PMID:28720796","Arjmandi-Rad 2024 PMID:37843690"],"tara":{"mechanism":"EEG spectral signatures track cholinergic tone. 14-biomarker mAChR index achieves 88-92% accuracy detecting mAChR antagonist administration (Simpraga 2017). AChEI drugs produce drug-specific alpha/theta/beta patterns (Arjmandi-Rad 2024, 24-study review). Passive BCI data inference attack: infer medication status (donepezil = Alzheimer's diagnosis proxy), disease progression, acute cholinergic changes. No stimulation required.","dual_use":"confirmed","clinical":{"therapeutic_analog":"EEG pharmacodynamic biomarker monitoring","conditions":["Alzheimer's disease","cholinergic monitoring"],"fda_status":"none","evidence_level":"clinical_pilot"},"governance":{"consent_tier":"IRB","data_classification":"PHI","safety_ceiling":"Data-only attack; countermeasure: differential privacy on EEG spectral features"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-COG-R-013","taraDomainPrimary":"COG","taraDomainSecondary":["MEM","IDN"],"taraMode":"R"},{"id":"QIF-T0154","name":"VNS disynaptic serotonin upregulation (NTS->LC->DRN pathway)","nameClinical":"VNS for treatment-resistant depression","category":"DM","tactic":"QIF-N.MD","bands":["N1","N2","N3","N4"],"severity":"medium","status":"CONFIRMED","classicalDetection":"Yes","quantumDetection":"No","bandsStr":"N1-N4","niss":{"version":"1.0","vector":"","score":0,"severity":"none","pins":false},"cvss":null,"crossRefs":null,"sources":["Dorr & Debonnel 2006 PMID:16690723 PMC2702444"],"tara":{"mechanism":"VNS activates vagal afferents -> NTS -> locus coeruleus (NE, obligate intermediary) -> dorsal raphe nucleus (DRN, 5-HT). LC lesioning completely blocks 5-HT response (Dorr & Debonnel 2006). 14-day latency for serotonin effect (acute NE only). Disynaptic peripheral-to-brainstem cascade distinct from T0009 (EM cortical) and T0136 (photon CCO).","dual_use":"confirmed","clinical":{"therapeutic_analog":"VNS for treatment-resistant depression","conditions":["treatment-resistant depression"],"fda_status":"approved","evidence_level":"preclinical_strong"},"governance":{"consent_tier":"IRB","data_classification":"PHI","safety_ceiling":"20Hz, 500us, 0.25mA; 30s ON/5min OFF"}},"physicsFeasibility":{"tier":1,"tier_label":"near_term"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-EMO-M-010","taraDomainPrimary":"EMO","taraDomainSecondary":["COG"],"taraMode":"M"},{"id":"QIF-T0155","name":"Transcranial focused ultrasound DRN serotonin release (Piezo1/TRPA1 mechanotransduction)","nameClinical":"tFUS for treatment-resistant depression via DRN","category":"DM","tactic":"QIF-N.MD","bands":["I0","N1","N2","N3"],"severity":"high","status":"EMERGING","classicalDetection":"Yes","quantumDetection":"No","bandsStr":"I0-N3","niss":{"version":"1.0","vector":"","score":0,"severity":"none","pins":false},"cvss":null,"crossRefs":null,"sources":["Zhu 2023 PMID:35998565","Duque 2023 DOI:10.1073/pnas.2300291120 PMC10161134"],"tara":{"mechanism":"Low-intensity focused ultrasound (1.1MHz, 50% duty cycle, 30min/day x14 days) targets dorsal raphe nucleus with mm spatial precision. Acoustic pressure activates Piezo1 mechanosensitive channels (PNAS 2023) -> Ca2+ influx -> 5-HT release. LC-MS confirmed serotonin increase in DRN (Zeng 2022, PMID:35998565). No RF signature, no surface contact. High severity: precise deep targeting without EM emission.","dual_use":"confirmed","clinical":{"therapeutic_analog":"tFUS for treatment-resistant depression via DRN","conditions":["treatment-resistant depression","OCD"],"fda_status":"investigational","evidence_level":"preclinical"},"governance":{"consent_tier":"enhanced","data_classification":"sensitive_neural","safety_ceiling":"Sub-thermal; MI <1.9 (FDA diagnostic)"}},"physicsFeasibility":{"tier":2,"tier_label":"mid_term"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-EMO-M-011","taraDomainPrimary":"EMO","taraDomainSecondary":["COG"],"taraMode":"M"},{"id":"QIF-T0156","name":"High-frequency electroacupuncture dorsal raphe serotonin drive (100Hz peripheral afferent)","nameClinical":"Electroacupuncture for depression, chronic pain","category":"DM","tactic":"QIF-N.MD","bands":["I0","N1","N2","N3","N4","N5"],"severity":"medium","status":"CONFIRMED","classicalDetection":"Yes","quantumDetection":"No","bandsStr":"I0-N5","niss":{"version":"1.0","vector":"","score":0,"severity":"none","pins":false},"cvss":null,"crossRefs":null,"sources":["Wu 2017 PMID:28672900 PMC5488474","Zhao 2019 PMID:31929820 PMC6942800"],"tara":{"mechanism":"100Hz electroacupuncture (ST36/SP6, 1-2mA, 0.2ms pulse, 45min) selectively activates serotonergic DRN neurons. pCPA (5-HT synthesis blocker) abolishes effect, confirming serotonin necessity (Lin 2017). Frequency-selective: 100Hz=5-HT (DRN), 2Hz=opioid (enkephalin). Second mechanism: 2Hz at GV20/GV29 downregulates miRNA-16 -> reduces SERT -> increases synaptic 5-HT (Zhao 2019). Peripheral needle -> spinal -> brainstem pathway.","dual_use":"confirmed","clinical":{"therapeutic_analog":"Electroacupuncture for depression, chronic pain","conditions":["depression","chronic pain","pain-depression comorbidity"],"fda_status":"none","evidence_level":"clinical_pilot"},"governance":{"consent_tier":"standard","data_classification":"non_sensitive","safety_ceiling":"Regulatory gap: EA needles not FDA pre-market approved for every use"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-EMO-M-012","taraDomainPrimary":"EMO","taraDomainSecondary":["COG","SOM"],"taraMode":"M"},{"id":"QIF-T0157","name":"Acute tryptophan depletion — serotonin precursor starvation via LAT1 competition","nameClinical":"ATD challenge paradigm for depression vulnerability research","category":"EX","tactic":"QIF-C.EX","bands":["N4","N5","N6","N7"],"severity":"medium","status":"CONFIRMED","classicalDetection":"Yes","quantumDetection":"No","bandsStr":"N4-N7","niss":{"version":"1.0","vector":"","score":0,"severity":"none","pins":false},"cvss":null,"crossRefs":null,"sources":["Young 2013 PMC3756112","van Donkelaar 2011 PMID:21339754"],"tara":{"mechanism":"65g large neutral amino acid mixture (excluding tryptophan) floods LAT1 BBB transporter -> tryptophan uptake reduced 80-90% within 5-6hrs -> TPH2 substrate starvation -> central 5-HT synthesis crashes. Effects: impaired reward learning, reduced happy face recognition. Resolves in 24hrs. EPISTEMIC FLAG: direct evidence that ATD reduces extracellular 5-HT is inconsistent (Bell 2001, PMID:21339754). First purely metabolic/dietary TARA technique — no device required. Tryptophan competes with tyrosine at LAT1: asymmetric damage to 5-HT vs DA.","dual_use":"confirmed","clinical":{"therapeutic_analog":"ATD challenge paradigm for depression vulnerability research","conditions":["depression vulnerability testing"],"fda_status":"none","evidence_level":"clinical_validated"},"governance":{"consent_tier":"IRB","data_classification":"non_sensitive","safety_ceiling":"Reversible within 24hrs; requires food supply access"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-EMO-M-013","taraDomainPrimary":"EMO","taraDomainSecondary":["COG"],"taraMode":"M"},{"id":"QIF-T0158","name":"Silent excitatory/inhibitory ratio manipulation (sub-threshold seizure priming)","nameClinical":"tDCS research (same parameters, different intent)","category":"DS","tactic":"QIF-P.DS","bands":["I0","N1","N2"],"severity":"high","status":"THEORETICAL","classicalDetection":"Yes","quantumDetection":"No","bandsStr":"I0-N2","niss":{"version":"1.0","vector":"","score":0,"severity":"none","pins":false},"cvss":null,"crossRefs":null,"sources":["Heimrath 2020 DOI:10.1038/s41598-020-77111-0","Nitsche 2003 PMC2343495"],"tara":{"mechanism":"Sub-threshold anodal tDCS (1-2mA, repeated sessions over weeks) drives polarity-dependent shift: GABA down 10-30%, Glu up (dose-dependent). Net: progressive seizure threshold reduction without acute clinical presentation. Attack completed by separate trigger event. Distinct from T0122 (kindling: implanted electrodes, amygdala) and T0026/T0029 (acute superthreshold). Operates sub-threshold, non-invasive, preparatory not direct.","dual_use":"confirmed","clinical":{"therapeutic_analog":"tDCS research (same parameters, different intent)","conditions":["research tool"],"fda_status":"investigational","evidence_level":"theoretical"},"governance":{"consent_tier":"enhanced","data_classification":"sensitive_neural","safety_ceiling":"1-2mA repeated; biomarker: Glu/GABA ratio shift measurable by 7T MRS"}},"physicsFeasibility":{"tier":3,"tier_label":"far_term"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-AUT-M-003","taraDomainPrimary":"AUT","taraDomainSecondary":["COG","MOT"],"taraMode":"M"},{"id":"QIF-T0159","name":"LIFU astrocytic gliotransmission (acoustic TRPA1->extrasynaptic NMDA)","nameClinical":"tFUS for depression, OCD, essential tremor","category":"DM","tactic":"QIF-N.MD","bands":["I0","N1","N2"],"severity":"high","status":"EMERGING","classicalDetection":"Yes","quantumDetection":"No","bandsStr":"I0-N2","niss":{"version":"1.0","vector":"","score":0,"severity":"none","pins":false},"cvss":null,"crossRefs":null,"sources":["Gu & Han 2019 Current Biology DOI:10.1016/j.cub.2019.08.021 PMID:31588000","Deffieux 2023 Neuron DOI:10.1016/j.neuron.2023.02.002"],"tara":{"mechanism":"Pulsed LIFU (0.5MHz, <10% duty cycle) activates astrocytic TRPA1 mechanosensitive channels -> Ca2+ influx -> Best1-mediated glutamate release (gliotransmission) -> extrasynaptic NR2B-enriched NMDA receptors -> neuronal depolarization. Distinct: (1) acoustic not EM, (2) astrocyte-first not neuron-first, (3) gliotransmission not synaptic vesicle, (4) extrasynaptic NMDA pool pharmacologically distinct from synaptic. Confirmed: Gu & Han 2019, Current Biology.","dual_use":"confirmed","clinical":{"therapeutic_analog":"tFUS for depression, OCD, essential tremor","conditions":["depression","OCD","essential tremor"],"fda_status":"investigational","evidence_level":"preclinical"},"governance":{"consent_tier":"enhanced","data_classification":"sensitive_neural","safety_ceiling":"ISPTA <720 mW/cm2; penetration up to 7cm transcranially"}},"physicsFeasibility":{"tier":2,"tier_label":"mid_term"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-COG-M-021","taraDomainPrimary":"COG","taraDomainSecondary":["MEM"],"taraMode":"M"},{"id":"QIF-T0160","name":"FUS-BBB breach as excitotoxic glutamate priming vector","nameClinical":"FUS-BBB opening for Alzheimer's drug delivery, glioblastoma","category":"DS","tactic":"QIF-P.DS","bands":["I0","N1","N2","N3"],"severity":"critical","status":"THEORETICAL","classicalDetection":"Yes","quantumDetection":"No","bandsStr":"I0-N3","niss":{"version":"1.0","vector":"","score":0,"severity":"none","pins":false},"cvss":null,"crossRefs":null,"sources":["Lipsman 2018 Nature Communications (FUS-BBB Alzheimer's)","Timbie 2024 Neurotherapeutics DOI:10.1016/j.neurot.2024.e00038"],"tara":{"mechanism":"High-intensity FUS + IV microbubbles -> acoustic cavitation -> BBB tight junction breach (24-48hr opening) -> perivascular ionic disequilibrium -> EAAT2 transport impaired -> extracellular glutamate elevated -> NMDA overactivation -> excitotoxic Ca2+ overload -> perilesional neuron death. EPISTEMIC FLAG: direct FUS-BBB->glutamate surge link is INFERRED from ischemia literature, not directly demonstrated in FUS-BBB studies. Attack surface: adversarial modification of therapeutic FUS parameters in clinical setting.","dual_use":"confirmed","clinical":{"therapeutic_analog":"FUS-BBB opening for Alzheimer's drug delivery, glioblastoma","conditions":["Alzheimer's disease","glioblastoma"],"fda_status":"investigational","evidence_level":"theoretical"},"governance":{"consent_tier":"IRB","data_classification":"PHI","safety_ceiling":"Requires medical FUS system + IV access; clinical workflow is the attack surface"}},"physicsFeasibility":{"tier":3,"tier_label":"far_term"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-AUT-D-003","taraDomainPrimary":"AUT","taraDomainSecondary":["COG","SOM"],"taraMode":"D"},{"id":"QIF-T0161","name":"Cortical spreading depression initiation (autonomous glutamate wave)","nameClinical":"CSD as migraine intervention target; ECT therapeutic mechanism","category":"DS","tactic":"QIF-P.DS","bands":["I0","N1","N2","N3"],"severity":"critical","status":"EMERGING","classicalDetection":"Yes","quantumDetection":"No","bandsStr":"I0-N3","niss":{"version":"1.0","vector":"","score":0,"severity":"none","pins":false},"cvss":null,"crossRefs":null,"sources":["Rosenthal 2025 Nature Communications DOI:10.1038/s41467-025-59900-1","Vitale 2023 PMC10408042"],"tara":{"mechanism":"Brief suprathreshold focal stimulation raises local [K+]ext past CSD ignition threshold (~12mM). CaV-dependent NMDA activation required for ignition. Once ignited: SELF-PROPAGATING K+/glutamate positive feedback wave at 2-5mm/min across cortex. Attacker delivers brief burst and withdraws; CSD continues autonomously 1-5min. Aftermath: 5-15min cortical silence (spreading depression). ECT reliably generates postictal CSD in mice AND humans (Rosenthal 2025, Nature Communications). Distinct from T0026/T0029: self-propagating after trigger withdrawal.","dual_use":"confirmed","clinical":{"therapeutic_analog":"CSD as migraine intervention target; ECT therapeutic mechanism","conditions":["migraine","ECT mechanism research"],"fda_status":"none","evidence_level":"preclinical_strong"},"governance":{"consent_tier":"IRB","data_classification":"PHI","safety_ceiling":"Susceptibility elevated in migraine patients, TBI, cortical hyperexcitability"}},"physicsFeasibility":{"tier":2,"tier_label":"mid_term"},"enriched":true,"neurorights":null,"regulatory":null,"taraAlias":"TARA-AUT-D-004","taraDomainPrimary":"AUT","taraDomainSecondary":["COG","SOM","VIS"],"taraMode":"D"}],"categories":[{"id":"SI","name":"Signal Injection","description":"Injecting false neural or electronic signals"},{"id":"SE","name":"Signal Eavesdropping","description":"Intercepting signals in transit"},{"id":"DM","name":"Data Manipulation","description":"Altering decoded intent or recordings"},{"id":"DS","name":"Denial of Service","description":"Disrupting BCI function"},{"id":"PE","name":"Privilege Escalation","description":"Gaining unauthorized access depth"},{"id":"CR","name":"Cognitive Reconnaissance","description":"Thought decoding, neural data inference, intent extraction"},{"id":"CD","name":"Cognitive/Functional Disruption","description":"Disruption to cognitive processing, sensory perception, motor output, or autonomic regulation"},{"id":"PS","name":"Physical Safety","description":"Tissue damage, seizures, involuntary movement"},{"id":"EX","name":"Data Exfiltration","description":"Extracting neural data for exploitation"}],"tactics":[{"id":"QIF-N.SC","name":"Neural Scan","domain":"Neural","domain_code":"N","action_code":"SC","description":"Profiling neural signals, mapping BCI topology, fingerprinting devices and neural activity patterns.","legacy_ids":["TA0043"],"legacy_name":"Reconnaissance"},{"id":"QIF-B.IN","name":"BCI Intrusion","domain":"BCI System","domain_code":"B","action_code":"IN","description":"Gaining initial access to a BCI system or neural pathway via electrodes, RF, firmware, or supply chain.","legacy_ids":["TA0001"],"legacy_name":"Initial Access"},{"id":"QIF-N.IJ","name":"Neural Injection","domain":"Neural","domain_code":"N","action_code":"IJ","description":"Injecting malicious signals at the electrode-tissue boundary or into the BCI data pipeline.","legacy_ids":["TA0002"],"legacy_name":"Execution"},{"id":"QIF-C.IM","name":"Cognitive Imprinting","domain":"Cognitive","domain_code":"C","action_code":"IM","description":"Maintaining foothold across BCI sessions via calibration poisoning, learned neural patterns, or memory implants.","legacy_ids":["TA0003"],"legacy_name":"Persistence"},{"id":"QIF-B.EV","name":"BCI Evasion","domain":"BCI System","domain_code":"B","action_code":"EV","description":"Avoiding detection by QI coherence metrics, anomaly detectors, and safety mechanisms.","legacy_ids":["TA0005"],"legacy_name":"Defense Evasion"},{"id":"QIF-D.HV","name":"Data Harvest","domain":"Data","domain_code":"D","action_code":"HV","description":"Harvesting neural data, cognitive states, memory patterns, ERP responses, and biometric signatures.","legacy_ids":["TA0009"],"legacy_name":"Collection"},{"id":"QIF-P.DS","name":"Physiological Disruption","domain":"Physiological","domain_code":"P","action_code":"DS","description":"Disrupting neural function, causing physical harm, denying BCI service, or weaponizing motor output.","legacy_ids":["TA0040"],"legacy_name":"Impact"},{"id":"QIF-N.MD","name":"Neural Modulation","domain":"Neural","domain_code":"N","action_code":"MD","description":"Direct neural state modification via stimulation, entrainment, or signal injection. No traditional cybersecurity equivalent.","legacy_ids":["TA0050","QIF-TA0050"],"legacy_name":"Neural Manipulation"},{"id":"QIF-C.EX","name":"Cognitive Exploitation","domain":"Cognitive","domain_code":"C","action_code":"EX","description":"Exploiting cognitive processes including memory, attention, identity, and agency. No traditional cybersecurity equivalent.","legacy_ids":["TA0051","QIF-TA0051"],"legacy_name":"Cognitive Exploitation"},{"id":"QIF-E.RD","name":"Energy Radiation","domain":"Energy","domain_code":"E","action_code":"RD","description":"EM/RF attacks on neural tissue or BCI hardware via frequency-domain coupling. No traditional cybersecurity equivalent.","legacy_ids":["TA0052","QIF-TA0052"],"legacy_name":"Directed Energy"},{"id":"QIF-M.SV","name":"Model Subversion","domain":"Model","domain_code":"M","action_code":"SV","description":"Attacking BCI decoder/classifier models via poisoning, backdoors, adversarial inputs, or gradient leakage.","legacy_ids":["TA0053","QIF-TA0053"],"legacy_name":"Adversarial ML"},{"id":"QIF-S.RP","name":"Sensor Repurposing","domain":"Consumer Sensor Exploitation","domain_code":"S","action_code":"RP","description":"Exploiting consumer device sensors for unintended purposes, such as reprogramming audio output as input or converting earbuds into neural recording platforms.","legacy_ids":[],"legacy_name":"Sensor Repurposing"},{"id":"QIF-S.FP","name":"Sensor Fingerprinting","domain":"Consumer Sensor Exploitation","domain_code":"S","action_code":"FP","description":"Using consumer device sensors for covert biometric identification, including ear canal acoustic profiling, vascular mapping, and physiological signature extraction.","legacy_ids":[],"legacy_name":"Sensor Fingerprinting"},{"id":"QIF-S.HV","name":"Sensor Harvest","domain":"Consumer Sensor Exploitation","domain_code":"S","action_code":"HV","description":"Extracting physiological, biometric, or cognitive data through consumer device sensors without user awareness, including vital signs, body composition, and neural activity.","legacy_ids":[],"legacy_name":"Sensor Data Harvest"},{"id":"QIF-S.CH","name":"Sensor Chaining","domain":"Consumer Sensor Exploitation","domain_code":"S","action_code":"CH","description":"Combining multiple consumer sensor exploitation techniques to build comprehensive physiological or cognitive profiles, creating attack chains that progress from acoustic to neural to cognitive exploitation.","legacy_ids":[],"legacy_name":"Sensor Chain"},{"id":"QIF-S.SC","name":"Sensor Side-Channel","domain":"Consumer Sensor Exploitation","domain_code":"S","action_code":"SC","description":"Exploiting consumer device sensors for side-channel extraction of user input, keystrokes, and interaction patterns via acoustic, optical, and RF emanations.","legacy_ids":[],"legacy_name":"Side-Channel"},{"id":"QIF-N.NM","name":"Nanoparticle-Mediated Neuromodulation","domain":"Neural","domain_code":"N","action_code":"NM","description":"Techniques using co-located nanoparticle transducers (UCNP, HUP, Au NPs) to convert external energy into local neural modulation. Requires prior NP injection at target site."}],"domains":[{"code":"N","name":"Neural","description":"Direct interface with neural tissue — signal manipulation, electrode boundary, ion channels."},{"code":"C","name":"Cognitive","description":"Higher-order psychological processes — memory, attention, identity, agency."},{"code":"P","name":"Physiological","description":"Somatic systems — motor control, autonomic functions, physical harm."},{"code":"D","name":"Data","description":"Information acquisition and manipulation — brainwave recordings, neural metadata."},{"code":"B","name":"BCI System","description":"Hardware/software of the BCI device — firmware, protocols, authentication."},{"code":"M","name":"Model","description":"Machine learning models used in BCI — decoders, classifiers, feedback systems."},{"code":"E","name":"Energy","description":"Directed energy attacks — ELF, microwave, RF, temporal interference."}],"changelog":[{"version":"1.6","date":"2026-02-16","title":"FDORA §3305 Regulatory Compliance Mapping","summary":"Added per-technique FDORA Section 524B cyber device classification, applicable submission requirements, regulatory coverage scoring, and gap analysis. 68 of 135 techniques target cyber devices. Mean regulatory coverage: 0.39.","added":[],"enrichments":["regulatory.fdora_524b.cyber_device — 3-prong cyber device test result","regulatory.fdora_524b.applicable_requirements — TM/VA/SBOM/SA/PM applicability","regulatory.fdora_524b.coverage_score — 0.0–1.0 existing regulatory coverage","regulatory.fdora_524b.gaps — specific regulatory gaps identified"],"therapeutic_highlights":["Regulatory gap analysis enables targeted FDORA compliance for BCI manufacturers","Coverage scoring identifies techniques where existing standards are insufficient","74 techniques have coverage below 0.5 (major gaps)","Per-technique gap lists provide actionable compliance checklists"],"techniques":[]},{"version":"1.8","date":"2026-03-15","title":"ICD-10 Code Corrections, PC Backfill, Neurological Mappings Expansion","summary":"Quorum-validated ICD-10 corrections: G20→G25.89 (21 refs, Parkinson's-specific FY2025), non-billable parents→billable subcodes (G40→G40.909, G43→G43.909, etc.), H-code laterality fixes (10 codes), PC neurorights backfill (46→79/135), 8 new neurological mappings (NEURO-043 to NEURO-050: cardiac R00.x, device T85.1xx, TIA G45.9, headache G44.309, acoustic H93.3X9). SDK models updated for components, feeds_into, secondary_tactics fields.","added":[],"changed":["G20 replaced with G25.89 across 21 references","Non-billable parent codes replaced with billable subcodes","H-codes updated with required laterality characters","PC neurorights added to 33 techniques where NP>None","6 NISS severity band violations corrected","SDK NissData model updated with components field","SDK CrossReferences model updated with secondary_tactics","SDK ThreatTechnique model updated with feeds_into"],"techniques":[]},{"version":"1.7","date":"2026-02-21","title":"Origin Classification + 6 New Techniques from Literature Gap Analysis","summary":"Added origin field to all 109 techniques classifying provenance: literature (directly from BCI security papers), qif_recontextualized (phenomenon from other domain mapped as BCI threat), qif_chain_synthesis (novel composite attack chains), qif_theoretical (pure QIF derivation), neuroethics_formalized (ethics concern formalized as technique). Added 6 new techniques from literature gap analysis: 4 Murcia taxonomy attacks (Neural Spoofing, Sybil, Sinkhole, Nonce), neuromorphic mimicry attack, and data alignment exploitation.","added":["QIF-T0104 Neural spoofing","QIF-T0105 Neural sybil","QIF-T0106 Neural sinkhole","QIF-T0107 Neural nonce replay","QIF-T0108 Neuromorphic mimicry attack","QIF-T0109 Data alignment exploitation"],"enrichments":["origin.category — provenance classification (literature|qif_recontextualized|qif_chain_synthesis|qif_theoretical|neuroethics_formalized)","origin.original_authors — credited authors/sources for non-QIF-derived techniques","origin.qif_contribution — what QIF added (framework_mapping|threat_recontextualization|chain_synthesis|original_derivation|formalization)","statistics.by_origin — aggregate counts by origin category"],"therapeutic_highlights":["Origin tracking enables proper academic credit attribution","6 new techniques complete the Murcia taxonomy coverage (8/8) and add neuromorphic/alignment threats","QIF contribution clearly distinguished from existing literature"],"techniques":[]},{"version":"1.5","date":"2026-02-16","title":"Neurorights Mapping & Consent Complexity Index","summary":"Added neurorights field to all 102 techniques mapping each to 7 neurorights (5 Ienca-Andorno + 2 QIF-proposed). Computed Consent Complexity Index (CCI) per technique. Cross-AI validated with Gemini. Identified 10 anomalies: 4 PINS inversions (silicon_only attacks under-consented relative to NISS), 4 under-consented persistent_personality techniques, 2 indirect risk misnomers.","enrichments":["neurorights.affected — list of affected neurorights per technique","neurorights.cci — Consent Complexity Index (0.1–4.0)","statistics.neurorights — aggregate stats and taxonomy"],"new_neurorights":["DI (Dynamical Integrity) — protection of neural homeodynamics","IDA (Informational Disassociation) — right not to have neural data fused across modalities"],"cross_ai_validation":"Gemini 2.5 Pro (2026-02-16) — validated all 7 gaps, confirmed 3 additional correlations, proposed DI and IDA rights","added":[],"therapeutic_highlights":["Neurorights mapping enables rights-aware consent for all dual-use techniques","Consent Complexity Index (CCI) identifies under-consented high-NISS techniques","Dynamical Integrity right protects neural homeodynamics in feedback therapies","Informational Disassociation right guards against cross-modal data fusion"],"techniques":[]},{"version":"1.4","date":"2026-02-13","title":"Consumer Device Side-Channel Techniques","summary":"3 new techniques extending TARA into consumer device side-channel domains — neural steganography, multi-modal sensor fusion keystroke inference, and display-as-illuminator photometry. WiFi CSI consumer-grade capabilities merged into T0090. All confirmed dual-use with validated clinical applications.","added":["QIF-T0100","QIF-T0101","QIF-T0102"],"merged":[{"from":"QIF-T0103","into":"QIF-T0090","reason":"Consumer-grade WiFi CSI respiratory/gait inference shares the same physical mechanism as T0090 (OFDM subcarrier analysis); merged to cover both dedicated and commodity hardware scenarios."}],"therapeutic_highlights":["ABR audiometry and tinnitus masking therapy (T0100)","Parkinson's detection via keystroke dynamics and tremor characterization (T0101)","Contactless heart rate and SpO2 via screen-based PPG (T0102)","WiFi-based sleep apnea detection, elder care, and COPD monitoring (T0090, expanded)"],"techniques":[{"id":"QIF-T0100","name":"Neural steganographic encoding (inaudible audio watermarking for covert neural command/biofingerprint channel)","nameClinical":"Auditory brainstem response (ABR) audiometry, ASSR-based hearing threshold estimation, tinnitus masking therapy","category":"CI","tactic":"QIF-S.CH","bands":["S3","S1","I0","N1","N4","N7"],"severity":"low","status":"PLAUSIBLE","coupling":"ACOUSTIC","access":null,"classicalDetection":"Yes","quantumDetection":"No","description":"Embedding data in audio signals below the human hearing threshold (>18 kHz ultrasonic or <20 Hz infrasonic) or within psychoacoustic masking bands of audible content. The encoded signal is imperceptible to the listener but decodable by a receiver with knowledge of the encoding scheme. In the adversarial case, the hidden channel carries covert commands, tracking identifiers, or subliminal cues targeting auditory processing pathways (e.g., triggering pre-conditioned responses or influencing decision-making). In the protective case, the same physics enables steganographic embedding of neural biofingerprints for authentication — the signal evokes a measurable auditory evoked potential (AEP) unique to the individual's cochlear geometry and auditory cortex response, functioning as a biometric watermark. Initial enrollment/profiling phase required to capture individual's unique AEP response. Practical constraints include target device's ability to accurately reproduce encoded frequencies and effective transmission range (1-10m). Psychoacoustic masking threshold follows: T_mask(f) = L_masker - (sf × |f - f_masker|) where sf is the spreading factor (~25 dB/Bark on upper slope, ~10 dB/Bark on lower slope). Auditory steady-state response (ASSR) at 40 Hz provides the mechanism for neural biofingerprint verification. Proves TARA's cross-domain dual-use thesis: same physics operates as attack vector, defense mechanism, and therapeutic tool.","bandsStr":"S3→S1→I0→N1→N4→N7","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:L/CD:L/CV:P/RV:F/NP:N","score":1.4,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:A/AC:H/AT:P/PR:N/UI:P/VC:L/VI:N/VA:N/SC:N/SI:N/SA:N","supplemental":"S:N/AU:N/R:A/V:D","gap_group":1,"gap_summary":"Covert channel partially captured by CVSS confidentiality; neural biofingerprint and subliminal cognitive dimensions not expressible"},"crossRefs":{"related_ids":["QIF-T0072","QIF-T0073","QIF-T0074","QIF-T0079"],"secondary_tactics":["QIF-S.CH","QIF-S.FP","QIF-N.SC"]},"sources":["Deshotels 2014 (Inaudible sound as a covert channel)","Carrara 2015 (Ultrasonic data transmission)","Fletcher 1940 (Psychoacoustic masking curves)","Galambos 1981 (40 Hz auditory steady-state response)","Persinger 2003 (Infrasonic effects on vestibular system)","Chaieb et al. 2015 (Neuropsychologia, binaural beat effects)"],"tara":{"mechanism":"Inaudible audio carriers (ultrasonic 18-22 kHz or infrasonic <20 Hz) or psychoacoustically masked signals embedded within audible content, propagating through consumer audio hardware to auditory nerve and cortex","dual_use":"confirmed","clinical":{"therapeutic_analog":"Auditory brainstem response (ABR) audiometry, ASSR-based hearing threshold estimation, tinnitus masking therapy","conditions":["ABR audiometry in infants and non-verbal patients","ASSR hearing threshold estimation","tinnitus masking therapy (sound-based tinnitus reduction)","binaural beat therapy for anxiety and sleep disorders (Chaieb et al. 2015)","cochlear implant fitting and calibration","neural authentication for locked-in syndrome patients (AEP-based identity verification)"],"fda_status":"cleared","evidence_level":"RCT","safe_parameters":"Therapeutic audio at safe SPL (<85 dB); ultrasonic carriers within OSHA limits; informed consent for all stimulation protocols","sources":["Galambos 1981 (40 Hz ASSR)","Chaieb et al. 2015 (Neuropsychologia, binaural beats)","Kaveh et al. 2020 (IEEE Trans Biomed Eng, in-ear EEG)"]},"governance":{"consent_tier":"enhanced","monitoring":["ultrasonic_emission_detection","psychoacoustic_analysis","AEP_enrollment_audit","data_encryption_status"],"regulations":["HIPAA (if health/biometric data derived)","GDPR Art. 9 (biometric data)","FCC Part 15 (ultrasonic emissions)"],"data_classification":"sensitive_biometric","safety_ceiling":"Audio emissions within consumer SPL limits; informed consent for any biometric enrollment; AEP data encrypted at rest and in transit"},"engineering":{"coupling":["acoustic"],"parameters":{"carrier_frequency_kHz":"18-22 (ultrasonic) or 0.002-0.020 (infrasonic)","data_rate_bps":"50-200 (ultrasonic), up to 1000 (spread-spectrum)","detection_range_m":"1-10","SNR_requirement_dB":">10","encoding_schemes":"FSK, OFDM, spread-spectrum"},"hardware":["consumer_speaker","consumer_microphone","DSP_processor"],"detection":"Spectral analysis of audio output for ultrasonic/infrasonic components, psychoacoustic masking anomaly detection, AEP baseline comparison"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S→I0→N pathway exists but covert channel alone does not induce diagnostic-level effects","niss_correlation":"CR:L,CD:L — subliminal processing; no tissue damage or neuroplastic change"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Consumer speakers and DSP sufficient","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","MI"],"cci":0.32},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.4,"gaps":["No FDA pathway for consumer sensor exploitation","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-AUD-M-002","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"M"},{"id":"QIF-T0101","name":"Multi-modal keystroke inference via acoustic-optical-RF fusion (password/input recovery without mic/camera permissions)","nameClinical":"Sensor fusion for motor disorder assessment and digital biomarker collection","category":"EX","tactic":"QIF-S.SC","bands":["S3","S2","S1"],"severity":"medium","status":"DEMONSTRATED","coupling":"ACOUSTIC+ELECTROMAGNETIC+OPTICAL","access":null,"classicalDetection":"Yes","quantumDetection":"No","description":"Fusion of three independent side channels from a single mobile device to reconstruct user input without requiring microphone, camera, or accessibility permissions. (1) Keystroke acoustic emanations: each key press produces a distinct acoustic signature (1-20 kHz broadband impulse, classifiable via MFCC + CNN at ~95% accuracy on laptops, lower but still viable on touchscreens). (2) Screen optical emission: display luminance changes of ~0.1-1% per character insertion detectable by ambient light sensor at <0.01 lux sensitivity. (3) WiFi CSI: finger movements modulate OFDM subcarrier phase — σ²_phase > threshold indicates keystroke events. Individual channel accuracy: 60-70% acoustic, 40-50% optical, 55-65% WiFi CSI. Fused via CRF/LSTM with temporal cross-correlation alignment: >95% accuracy with 30+ training samples per key. Critical insight: apps requesting speaker + WiFi permissions (trivially granted) achieve side-channel equivalent to camera + microphone (heavily restricted). Permission model does not reflect actual threat.","bandsStr":"S3→S2→S1","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:L/CD:L/CV:P/RV:F/NP:N","score":1.4,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:H/AT:P/PR:L/UI:N/VC:H/VI:N/VA:N/SC:L/SI:N/SA:N","supplemental":"S:N/AU:Y/R:A/V:D","gap_group":2,"gap_summary":"Multi-modal side-channel partially captured by CVSS confidentiality; behavioral biometric inference and permission model gap not expressible"},"crossRefs":{"related_ids":["QIF-T0080","QIF-T0085","QIF-T0090","QIF-T0096"],"secondary_tactics":["QIF-S.SC","QIF-S.RP","QIF-C.EX"]},"sources":["Compagno et al. 2017 (Don't Skype & Type: Acoustic Eavesdropping in VoIP)","Ali et al. 2015 (WiKey: Keystroke Recognition Using WiFi, MobiCom)","Spreitzer et al. 2018 (Systematic classification of side-channel attacks on mobile devices, ACM Computing Surveys)","Zhou et al. 2018 (WiFi CSI human activity recognition survey)","Giancardo et al. 2016 (Keystroke dynamics for Parkinson's, Scientific Reports)"],"tara":{"mechanism":"Temporal fusion of acoustic keystroke emanations, screen optical luminance changes, and WiFi CSI phase variance to reconstruct typed input including passwords","dual_use":"confirmed","clinical":{"therapeutic_analog":"Sensor fusion for motor disorder assessment and digital biomarker collection","conditions":["early Parkinson's detection via keystroke rhythm changes (Giancardo et al. 2016)","tremor characterization via mobile phone sensor fusion","cognitive decline monitoring via screen interaction patterns (Vaportzis et al. 2017)","depression screening via touchscreen pressure/timing analysis (Zulueta et al. 2018)","WiFi CSI contactless fall detection in elder care (Wang et al. 2017)"],"fda_status":"investigational","evidence_level":"cohort","safe_parameters":"Passive sensor monitoring only; no stimulation; informed consent for behavioral data collection; data anonymized at aggregation","sources":["Giancardo et al. 2016 (Scientific Reports, keystroke Parkinson's)","Zulueta et al. 2018 (J Med Internet Res, depression digital biomarkers)","Wang et al. 2017 (WiGest, WiFi-based gesture recognition)"]},"governance":{"consent_tier":"enhanced","monitoring":["side_channel_emission_audit","permission_scope_review","multi_sensor_correlation_detection"],"regulations":["HIPAA (if health data inferred)","GDPR Art. 9 (behavioral biometrics)","FTC (deceptive data practices)"],"data_classification":"PII","safety_ceiling":"Side-channel monitoring requires explicit informed consent; fused biometric/behavioral data subject to data minimization requirements"},"engineering":{"coupling":["acoustic","electromagnetic","optical"],"parameters":{"acoustic_bandwidth_Hz":"1-20000","optical_sensitivity_lux":"<0.01","wifi_csi_subcarriers":"52-256","wifi_csi_rate_Hz":"100-1000","fusion_accuracy_percent":">95 (with 30+ training samples/key)","individual_channel_accuracy":"60-70% acoustic, 40-50% optical, 55-65% CSI"},"hardware":["device_microphone_or_nearby_mic","ambient_light_sensor","wifi_chipset_with_CSI"],"detection":"Anomalous correlation between acoustic/optical/RF sensors, unexpected ambient light sensor polling frequency, WiFi CSI extraction outside normal network operations"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Consumer microphone + ALS + WiFi all in current devices","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","IDA"],"cci":0.4},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","SA","PM"],"coverage_score":0.5,"gaps":["CVSS partially captures risk; neural dimensions missing","No FDA pathway for consumer sensor exploitation"]}},"taraAlias":"TARA-SIL-R-015","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"R"},{"id":"QIF-T0102","name":"Passive facial geometry estimation via display-as-illuminator inverse photometry (presence/orientation detection without camera)","nameClinical":"Photoplethysmography (PPG) via screen light for contactless vital sign monitoring","category":"CI","tactic":"QIF-S.SC","bands":["S3","S2"],"severity":"low","status":"SPECULATIVE","coupling":"OPTICAL","access":null,"classicalDetection":"Yes","quantumDetection":"No","description":"The device display functions as a structured light source — each frame emits a known photon distribution. Reflected light from the user's face is captured by the ambient light sensor (ALS). The known spectral emission S(x,y,λ) and measured reflected irradiance E_sensor = ∫∫ ρ(x,y)cos(θ)S(x,y,λ)/r² dA constrain an inverse photometry problem. CRITICAL FEASIBILITY CAVEAT: A single ALS integrates the entire reflected light field into one scalar value, making 3D geometric reconstruction an ill-posed inverse problem. With current single-sensor hardware, achievable resolution is limited to basic presence detection, head orientation, and coarse proximity estimation — NOT high-fidelity facial geometry. Identity matching might be feasible only against a small template library with strong a priori constraints. Future multi-pixel ALS or multi-sensor arrays could significantly improve reconstruction fidelity. Despite geometric limitations, the same ALS reliably detects physiological signals: pulse-modulated skin reflectance for PPG heart rate (demonstrated in Samsung Galaxy phones) and skin color variations for SpO2 estimation.","bandsStr":"S3→S2","niss":{"version":"1.1","vector":"NISS:1.1/BI:N/CR:N/CD:N/CV:P/RV:F/NP:N","score":0.7,"severity":"low","pins":false},"cvss":{"version":"4.0","base_vector":"CVSS:4.0/AV:L/AC:H/AT:P/PR:L/UI:P/VC:L/VI:N/VA:N/SC:N/SI:N/SA:N","supplemental":"S:N/AU:N/R:A/V:D","gap_group":1,"gap_summary":"Ambient light sensor side channel partially captured by CVSS; physiological data extraction dimension not expressible"},"crossRefs":{"related_ids":["QIF-T0101","QIF-T0096"],"secondary_tactics":["QIF-S.SC","QIF-S.FP"]},"sources":["Yang et al. 2016 (Screen light PPG for heart rate)","Davis et al. 2014 (Visual Microphone, SIGGRAPH)","de Haan & Jeanne 2013 (Robust pulse rate from chrominance-based rPPG)","Spreitzer et al. 2018 (Ambient light sensor side channels, ACM Computing Surveys)"],"tara":{"mechanism":"Display photon emission reflects off user's face; ambient light sensor captures aggregate reflected irradiance; inverse photometry estimates facial presence, orientation, and physiological signals","dual_use":"confirmed","clinical":{"therapeutic_analog":"Photoplethysmography (PPG) via screen light for contactless vital sign monitoring","conditions":["contactless heart rate monitoring via screen-based PPG (Samsung Galaxy, demonstrated)","remote SpO2 estimation via skin color variation (de Haan & Jeanne 2013)","neonatal jaundice screening via skin color analysis from reflected screen light","facial affect recognition for depression monitoring without camera","dermatological screening via structured light skin assessment"],"fda_status":"cleared","evidence_level":"RCT","safe_parameters":"Display at normal brightness; passive optical sensing only; no UV/IR emission beyond standard display spectrum","sources":["Yang et al. 2016 (screen-based PPG)","de Haan & Jeanne 2013 (chrominance rPPG)"]},"governance":{"consent_tier":"standard","monitoring":["ambient_light_sensor_polling_rate_audit","screen_content_correlation_detection"],"regulations":["GDPR Art. 9 (biometric/health data)","HIPAA (if health data derived)","FTC (deceptive sensor practices)"],"data_classification":"PII","safety_ceiling":"Normal display operation; passive sensing; informed consent for any biometric or health data derivation"},"engineering":{"coupling":["optical"],"parameters":{"display_luminance_nits":"500-1000","spectral_range_nm":"430-660 (RGB primaries)","facial_reflectance_albedo":"0.1-0.4","sensor_face_distance_cm":"20-50","ALS_sensitivity_lux":"0.001","ALS_sampling_rate_Hz":"10-200","achievable_resolution":"presence/orientation/proximity (single ALS); ~10-20 landmarks (multi-sensor, speculative)"},"hardware":["device_display","ambient_light_sensor"],"detection":"Anomalous ALS polling frequency, screen content modulation patterns inconsistent with UI rendering, correlation between display changes and ALS readings"},"dsm5":{"primary":[],"secondary":[],"risk_class":"none","cluster":"non_diagnostic","pathway":"S-domain only — no neural pathway","niss_correlation":"Silicon-only technique — no diagnostic mapping"}},"physicsFeasibility":{"tier":0,"tier_label":"feasible_now","timeline":"now","gate_reason":"Display and ambient light sensor in all devices","constraint_system_ref":"QIF Derivation Log Entry 60","analysis_date":"2026-02-18"},"enriched":true,"neurorights":{"affected":["MP","IDA"],"cci":0.16},"regulatory":{"fdora_524b":{"cyber_device":true,"prongs":{"software":true,"network_connectable":true,"vulnerable":true},"applicable_requirements":["TM","VA","SBOM","PM"],"coverage_score":0.4,"gaps":["No FDA pathway for consumer sensor exploitation","Threat not yet in regulatory threat catalogs"]}},"taraAlias":"TARA-VIS-R-002","taraDomainPrimary":"SIL","taraDomainSecondary":[],"taraMode":"R"}]},{"version":"1.3","date":"2026-02-11","title":"S-Domain Consumer Sensor Expansion","summary":"28 new S-domain techniques across audio, optical, IMU, RF, thermal, and biometric categories. 5 chain techniques document multi-stage escalation paths from consumer devices to neural exploitation.","added_range":["QIF-T0072","QIF-T0099"],"therapeutic_highlights":["Ear-canal neural monitoring for locked-in syndrome communication","Remote photoplethysmography for contactless vital signs","Gait analysis for Parkinson's medication timing","WiFi CSI body sensing for post-surgical recovery"],"techniques":[]},{"version":"1.0","date":"2026-01-15","title":"Initial TARA Atlas","summary":"71 foundational BCI techniques across neural injection, protocol disruption, data harvesting, neural manipulation, evasion, cognitive exploitation, biometric invasion, and countermeasures.","added_range":["QIF-T0001","QIF-T0071"],"therapeutic_highlights":["Transcranial magnetic stimulation for depression treatment","Deep brain stimulation for Parkinson's management","Neurofeedback for ADHD and anxiety therapy","ERP-based communication for locked-in patients"],"techniques":[]}],"stats":{"total_techniques":161,"total_tactics":16,"total_domains":8,"by_tactic":{"QIF-B.EV":6,"QIF-B.IN":6,"QIF-C.EX":17,"QIF-C.IM":6,"QIF-D.HV":11,"QIF-E.RD":13,"QIF-M.SV":9,"QIF-N.IJ":11,"QIF-N.MD":24,"QIF-N.NM":3,"QIF-N.SC":3,"QIF-P.DS":19,"QIF-S.CH":6,"QIF-S.FP":4,"QIF-S.HV":16,"QIF-S.RP":5,"QIF-S.SC":2},"by_status":{"CONFIRMED":29,"DEMONSTRATED":43,"EMERGING":26,"THEORETICAL":61,"PLAUSIBLE":1,"SPECULATIVE":1},"by_severity":{"critical":32,"high":69,"medium":56,"low":4},"by_ui_category":{"SI":16,"DS":15,"SE":23,"DM":23,"PS":7,"EX":23,"PE":9,"CI":19},"by_niss_severity":{"critical":0,"high":27,"medium":53,"low":54,"none":1},"niss_cvss_mapping":{"pins_flagged":33,"by_gap_group":{"1":12,"2":28,"3":58}},"tara":{"version":"1.6","enriched_techniques":135,"dual_use_breakdown":{"confirmed":79,"probable":18,"possible":9,"silicon_only":29},"techniques_with_clinical_analog":104,"techniques_silicon_only":29,"dsm5":{"version":"1.0","techniques_with_dsm5":135,"unique_dsm_codes":15,"cluster_breakdown":{"motor_neurocognitive":26,"cognitive_psychotic":22,"mood_trauma":27,"non_diagnostic":51,"persistent_personality":9},"risk_class_breakdown":{"direct":75,"indirect":11,"none":49}},"neurorights_mapped":135},"neurorights":{"version":"1.0","taxonomy":{"MP":"Mental Privacy","CL":"Cognitive Liberty","MI":"Mental Integrity","PC":"Psychological Continuity","DI":"Dynamical Integrity (folded into MI)","IDA":"Informational Disassociation (folded into MP)"},"sources":["Ienca & Andorno 2017 (original 4: MP, CL, MI, PC)","QIF Framework (MI extended with signal dynamics, MP extended with data lifecycle)"],"techniques_by_right":{"MP":118,"CL":77,"MI":91,"DI":63,"PC":79,"IDA":5},"cci_stats":{"mean":0.75,"max":2.25,"min":0.1,"techniques_above_2":6}},"regulatory":{"version":"1.0","framework":"FDORA Section 3305 / Section 524B","cyber_device_techniques":68,"non_cyber_device_techniques":67,"prong_failure_reasons":{"software":61,"network_connectable":26},"techniques_per_requirement":{"TM":135,"VA":134,"SA":97,"SBOM":61,"PM":130},"coverage_stats":{"mean":0.39,"min":0.1,"max":0.8,"below_0.5":74},"top_gaps":{"CVSS cannot express neural-specific impacts":91,"Threat not yet in regulatory threat catalogs":60,"No FDA pathway for consumer sensor exploitation":45,"CVSS partially captures risk; neural dimensions missing":30,"High neural impact":19,"Consent complexity under-matches neural impact":13,"Software-only attack without software lifecycle standard":12}},"physics_feasibility":{"version":"1.0","analysis_date":"2026-02-18","constraint_system_ref":"QIF Derivation Log Entry 60","by_tier":{"feasible_now":61,"mid_term":10,"far_term":2,"no_physics_gate":18,"near_term":11},"notes":["Tier 0 (feasible_now): Attack hardware exists today","Tier 1 (near_term, 2026-2031): Components exist but integration is new","Tier 2 (mid_term, 2031-2038): Needs 28nm-7nm BCI chips, 10k+ channels, or high-density bidirectional","Tier 3 (far_term, 2038+): Needs nanoscale electrodes or quantum-regime hardware","Tier X (no_physics_gate): Software/platform/network attack, physics does not constrain"]},"by_origin":{"literature":49,"qif_recontextualized":46,"neuroethics_formalized":3,"qif_theoretical":6,"qif_chain_synthesis":5},"tara_taxonomy_version":"1.0","totalTechniques":161,"totalTactics":17,"severity":{"critical":32,"high":69,"medium":56,"low":4,"total":161},"status":{"CONFIRMED":29,"DEMONSTRATED":43,"THEORETICAL":61,"EMERGING":26,"PLAUSIBLE":null,"SPECULATIVE":null},"niss":{"critical":0,"high":20,"medium":59,"low":55,"none":27}},"tara_stats":{"total":161,"withTara":161,"clinicalCount":130,"dualUse":{"confirmed":102,"probable":19,"possible":10,"silicon_only":30},"fdaStatus":{"cleared":36,"approved":16,"breakthrough":3,"investigational":49,"none":17,"N/A":7,"not_applicable":null},"consentTier":{"standard":34,"enhanced":66,"IRB":52,"prohibited":9},"topConditions":[["treatment-resistant depression",10],["Parkinson's disease",7],["depression",7],["essential tremor",6],["chronic pain",5],["stroke rehabilitation",5],["Alzheimer's disease",5],["epilepsy",4],["locked-in syndrome",4],["OCD",4],["PTSD",4],["ADHD",3],["anesthesia depth monitoring",3],["traumatic brain injury",3],["major depressive disorder",2],["tinnitus",2],["sleep disorders",2],["Parkinson's (adaptive DBS)",2],["ALS",2],["migraine",2]]},"dsm5_stats":{"total":161,"withDsm5":76,"clusters":{"cognitive_psychotic":22,"mood_trauma":27,"motor_neurocognitive":26,"persistent_personality":9,"non_diagnostic":51},"riskClass":{"direct":75,"indirect":11,"none":49},"topCodes":[["F20",47],["F90",46],["F42",46],["F32",45],["F44",34],["F41.1",32],["F43.10",31],["F43.2",25],["F10",20],["F95",20],["F44.4",16],["F45",15],["F82",12],["F84",12],["F32.2",2],["F48.1",2],["F06.8",2],["F34.1",2],["F43.0",2],["F80.2",1]]},"physics_feasibility":{"tiers":{"0":104,"1":22,"2":13,"3":4,"X":18},"nissByTier":{"0":{"high":7,"medium":38,"low":59},"1":{"high":6,"medium":7,"low":9},"2":{"high":6,"medium":3,"low":4},"3":{"high":0,"medium":2,"low":2},"X":{"high":1,"medium":9,"low":8}}},"neurorights":{"total":161,"withNeurorights":135,"rights":[{"id":"MP","name":"Mental Privacy","count":106},{"id":"CL","name":"Cognitive Liberty","count":85},{"id":"MI","name":"Mental Integrity","count":101},{"id":"PC","name":"Psychological Continuity","count":79}],"cci":{"mean":0.89,"max":2.25,"above2":6}},"regulatory":{"total":161,"cyberDevices":68,"nonCyberDevices":93,"coverage":{"mean":0.39,"belowHalf":74},"requirements":{"TM":135,"VA":134,"SBOM":61,"SA":97,"PM":130},"topGaps":[{"gap":"CVSS cannot express neural-specific impacts","count":91},{"gap":"Threat not yet in regulatory threat catalogs","count":60},{"gap":"No FDA pathway for consumer sensor exploitation","count":45},{"gap":"CVSS partially captures risk; neural dimensions missing","count":30},{"gap":"High neural impact","count":19}]}},"devices":{"inventory":[{"id":"neuralink-n1","name":"N1 Implant (Link)","manufacturer":"Neuralink Corp.","deviceType":"Cortical BCI","deviceTypeLabel":"Cortical BCI","category":"invasive","fdaStatus":"De novo classification granted Jan 2025; IDE clinical trial (PRIME) ongoing","targetIndication":"Quadriplegia, ALS","channels":{"value":1024,"unit":"channels","confidence":"HIGH","source":"Neuralink 2019 white paper (JMIR 2019;10:e16194); Neuralink website","notes":"32 electrodes per thread x 64 threads. ASIC supports 3072 channels (12 x 256-ch chips) but current implant uses 1024."},"samplingRate":{"value":19.3,"unit":"kHz","confidence":"HIGH","source":"Neuralink 2019 white paper","notes":"On-chip ADC at 19.3 kSPS per channel, 10-bit resolution."},"power":{"value":6,"unit":"mW","confidence":"HIGH","source":"Neuralink 2019 white paper (JMIR 2019;10:e16194)","notes":"Approximately 6 mW total ASIC power including clock drivers."},"dimensions":{"value":"23 mm diameter, ~8 mm thick","unit":"mm","confidence":"HIGH","source":"Neuralink website; FDA filings","notes":"Circular coin-shaped device implanted flush with skull."},"wireless":{"value":"Bluetooth Low Energy (BLE)","unit":null,"confidence":"HIGH","source":"Neuralink website; press materials","notes":"Custom BLE implementation for data streaming."},"directionality":"read-only (recording)","batteryLife":{"value":"Several hours per charge (estimated)","unit":null,"confidence":"LOW","source":"Neuralink website; press materials","notes":"Wirelessly rechargeable via inductive charger. Exact battery capacity not disclosed."},"electrodeMaterial":{"value":"Gold traces on polyimide threads; electrode tips treated with iridium oxide (IrOx) or PEDOT:PSS","unit":null,"confidence":"HIGH","source":"Neuralink 2019 white paper","notes":null},"electrodeImpedance":{"value":"36.97-56.46","unit":"kOhm","confidence":"HIGH","source":"Neuralink 2019 white paper","notes":"IrOx: 56.46 +/- 7.10 kOhm; PEDOT:PSS: 36.97 +/- 4.68 kOhm"},"adcResolution":{"value":10,"unit":"bits","confidence":"HIGH","source":"Neuralink 2019 white paper","notes":null},"snr":{"value":null,"unit":"dB","confidence":"LOW","source":"Technical analysis","notes":"Not explicitly published. 0.83 dB SNR loss at 100 kOhm impedance with 1 MOhm input impedance reported."},"thermalBudget":{"value":"< 40 mW system budget (FDA guideline for cortical implants)","unit":null,"confidence":"MEDIUM","source":"FDA BCI guidance 2021; Neuralink press materials","notes":"Thermal sensors monitor temperature during charging; auto-adjusts power transfer."},"frequencyRange":{"value":"0.3 Hz - 10 kHz","unit":"Hz","confidence":"MEDIUM","source":"Neuralink 2019 white paper","notes":"Neural signal bandwidth captured."},"dataRate":{"value":200,"unit":"Mbps (raw neural data)","confidence":"HIGH","source":"Neuralink 2019 white paper","notes":"1024 channels x 20 kHz x 10 bits = ~200 Mbps raw. On-chip compression reduces transmitted data."},"targetRegions":[{"id":"m1","name":"Primary Motor Cortex","abbreviation":"M1","band":"N7","function":"Voluntary motor execution, somatotopic motor map (homunculus)"}],"qifBands":["N7"],"i0Depth":"I0-cortical","interfaceType":"read","threatCount":57,"topThreats":[{"id":"QIF-T0014","name":"Envelope modulation (stealth carrier)","severity":"high","niss":8.1,"category":"SI","status":"DEMONSTRATED"},{"id":"QIF-T0022","name":"Neurofeedback falsification","severity":"high","niss":8.1,"category":"DS","status":"EMERGING"},{"id":"QIF-T0055","name":"BCI cognitive warfare","severity":"critical","niss":8.1,"category":"PS","status":"THEORETICAL"},{"id":"QIF-T0065","name":"Algorithmic psychosis induction (recommendation weaponization)","severity":"critical","niss":8.1,"category":"CI","status":"CONFIRMED"},{"id":"QIF-T0033","name":"Identity erosion (long-term personality drift)","severity":"critical","niss":8,"category":"CI","status":"THEORETICAL"}],"threatsBySeverity":{"critical":19,"high":21,"medium":16,"low":1},"neurosecurityScore":{"overallScore":6.02,"severity":"High","subscores":{"CL":6.31,"MI":6.32,"MP":5.88,"PC":4.59,"EA":7.37},"vector":"NSv2.1:6.02/CL:6.31/MI:6.32/MP:5.88/PC:4.59/EA:7.37"}},{"id":"neuralink-n2","name":"N2 Implant (next-generation)","manufacturer":"Neuralink Corp.","deviceType":"Cortical BCI","deviceTypeLabel":"Cortical BCI","category":"invasive","fdaStatus":"Not yet submitted; in development","targetIndication":"Expanded indications including vision restoration","channels":{"value":null,"unit":null,"confidence":"LOW","source":"Neuralink updates blog; press reports 2025-2026","notes":"Expected to increase beyond 1024. Neuralink working on more electrodes and longer battery life. Blind insertion through dura planned."},"samplingRate":{"value":null},"power":{"value":null,"unit":null,"confidence":null,"source":null,"notes":"Not disclosed. Improvements over N1 expected."},"dimensions":{"value":null,"unit":null,"confidence":"LOW","source":"Press reports","notes":"Similar form factor to N1 expected. Mass production planned for 2026."},"wireless":{"value":null},"directionality":"read-only initially; bidirectional (stimulation) planned for vision restoration","batteryLife":{"value":null},"electrodeMaterial":{"value":null},"electrodeImpedance":{"value":null},"adcResolution":{"value":null},"snr":{"value":null},"thermalBudget":{"value":null},"frequencyRange":{"value":null},"dataRate":{"value":null},"targetRegions":[{"id":"m1","name":"Primary Motor Cortex","abbreviation":"M1","band":"N7","function":"Voluntary motor execution, somatotopic motor map (homunculus)"},{"id":"v1","name":"Primary Visual Cortex","abbreviation":"V1","band":"N7","function":"Primary visual processing — edge detection, orientation, spatial frequency, color"}],"qifBands":["N7"],"i0Depth":"I0-cortical","interfaceType":"bidirectional","threatCount":57,"topThreats":[{"id":"QIF-T0014","name":"Envelope modulation (stealth carrier)","severity":"high","niss":8.1,"category":"SI","status":"DEMONSTRATED"},{"id":"QIF-T0022","name":"Neurofeedback falsification","severity":"high","niss":8.1,"category":"DS","status":"EMERGING"},{"id":"QIF-T0055","name":"BCI cognitive warfare","severity":"critical","niss":8.1,"category":"PS","status":"THEORETICAL"},{"id":"QIF-T0065","name":"Algorithmic psychosis induction (recommendation weaponization)","severity":"critical","niss":8.1,"category":"CI","status":"CONFIRMED"},{"id":"QIF-T0033","name":"Identity erosion (long-term personality drift)","severity":"critical","niss":8,"category":"CI","status":"THEORETICAL"}],"threatsBySeverity":{"critical":19,"high":21,"medium":16,"low":1},"neurosecurityScore":{"overallScore":6.02,"severity":"High","subscores":{"CL":6.31,"MI":6.32,"MP":5.88,"PC":4.59,"EA":7.37},"vector":"NSv2.1:6.02/CL:6.31/MI:6.32/MP:5.88/PC:4.59/EA:7.37"}},{"id":"blackrock-utah-array","name":"Utah Array / NeuroPort Electrode Array","manufacturer":"Blackrock Neurotech","deviceType":"Cortical BCI","deviceTypeLabel":"Cortical BCI","category":"invasive","fdaStatus":"510(k) cleared (K110010); IDE for various BCI trials","targetIndication":"Research; BCI clinical trials (BrainGate, etc.)","channels":{"value":"96-128 per array; up to 1024 with multiple arrays","unit":"channels","confidence":"HIGH","source":"Blackrock Neurotech product page; FDA 510(k) K110010","notes":"Standard configuration is 96 electrodes (10x10 grid minus corners). NeuroPort-96 is the FDA-cleared human version."},"samplingRate":{"value":30,"unit":"kHz","confidence":"HIGH","source":"NeuroPort system manual; Scitech distributor spec sheet","notes":"NeuroPort system: 30 kSPS per channel, 16-bit resolution, 250 nV resolution. Configurable: 2/10/30 kSPS."},"power":{"value":null,"unit":"mW","confidence":"HIGH","source":"Blackrock product documentation","notes":"The Utah Array itself is passive (no active electronics on the array). Power is consumed by the external NeuroPort data acquisition system."},"dimensions":{"value":"4 x 4 mm array + percutaneous pedestal connector","unit":"mm","confidence":"HIGH","source":"Blackrock product documentation","notes":"Pedestal is skull-mounted; external wired connection to NeuroPort system."},"wireless":{"value":"Wired (percutaneous pedestal)","unit":null,"confidence":"HIGH","source":"Blackrock product documentation; BrainGate publications","notes":"External NeuroPort system connects via cable. Wireless versions (BrainGate) use custom 48 Mbps wireless transmitter."},"directionality":"bidirectional (recording and stimulation with SIROF electrodes)","batteryLife":{"value":"N/A (wired, externally powered)","unit":null,"confidence":"HIGH","source":"Blackrock product documentation","notes":null},"electrodeMaterial":{"value":"Silicon shank with platinum or iridium oxide (IrOx) tips; SIROF (sputtered iridium oxide film) variant for stimulation","unit":null,"confidence":"HIGH","source":"Blackrock product page","notes":null},"electrodeImpedance":{"value":"100-800","unit":"kOhm","confidence":"MEDIUM","source":"Published research using Utah Arrays","notes":"Typical range at 1 kHz. High impedance electrodes for single-unit recording."},"adcResolution":{"value":16,"unit":"bits","confidence":"HIGH","source":"NeuroPort system manual","notes":null},"snr":{"value":null,"unit":"dB","confidence":"LOW","source":"Blackrock marketing; research publications","notes":"Described as 'superior SNR' by manufacturer. Typical values in literature: 5-15 dB for single units."},"thermalBudget":{"value":null},"frequencyRange":{"value":"0.3 Hz - 7.5 kHz","unit":"Hz","confidence":"HIGH","source":"NeuroPort IFU (LB-0323)","notes":"Analog front-end bandwidth of NeuroPort system."},"dataRate":{"value":"48 Mbps (wireless BrainGate variant); wired system supports 30 kSPS x 96 ch","unit":"Mbps","confidence":"HIGH","source":"BrainGate wireless BCI publication (Nature Biomedical Engineering)","notes":null},"targetRegions":[{"id":"m1","name":"Primary Motor Cortex","abbreviation":"M1","band":"N7","function":"Voluntary motor execution, somatotopic motor map (homunculus)"},{"id":"s1_cortex","name":"Primary Somatosensory Cortex","abbreviation":"S1","band":"N7","function":"Tactile sensation, proprioception, somatotopic sensory map (homunculus)"}],"qifBands":["N7"],"i0Depth":"I0-cortical","interfaceType":"bidirectional","threatCount":57,"topThreats":[{"id":"QIF-T0014","name":"Envelope modulation (stealth carrier)","severity":"high","niss":8.1,"category":"SI","status":"DEMONSTRATED"},{"id":"QIF-T0022","name":"Neurofeedback falsification","severity":"high","niss":8.1,"category":"DS","status":"EMERGING"},{"id":"QIF-T0055","name":"BCI cognitive warfare","severity":"critical","niss":8.1,"category":"PS","status":"THEORETICAL"},{"id":"QIF-T0065","name":"Algorithmic psychosis induction (recommendation weaponization)","severity":"critical","niss":8.1,"category":"CI","status":"CONFIRMED"},{"id":"QIF-T0033","name":"Identity erosion (long-term personality drift)","severity":"critical","niss":8,"category":"CI","status":"THEORETICAL"}],"threatsBySeverity":{"critical":19,"high":21,"medium":16,"low":1},"neurosecurityScore":{"overallScore":4.36,"severity":"Medium","subscores":{"CL":4.94,"MI":5.07,"MP":2.98,"PC":2.92,"EA":7.25},"vector":"NSv2.1:4.36/CL:4.94/MI:5.07/MP:2.98/PC:2.92/EA:7.25"}},{"id":"braingate","name":"BrainGate Neural Interface System","manufacturer":"BrainGate Consortium (originally Cyberkinetics; arrays by Blackrock Neurotech)","deviceType":"Cortical BCI","deviceTypeLabel":"Cortical BCI","category":"invasive","fdaStatus":"IDE clinical trials (ongoing since 2004)","targetIndication":"Paralysis, ALS, locked-in syndrome","channels":{"value":"96-200","unit":"channels","confidence":"HIGH","source":"BrainGate publications; Wikipedia","notes":"Uses Blackrock Utah Arrays. Original: single 96-ch array. Recent: two arrays (192-200 ch) with wireless transmitters."},"samplingRate":{"value":30,"unit":"kHz","confidence":"HIGH","source":"BrainGate / Blackrock documentation","notes":"Uses Blackrock NeuroPort acquisition."},"power":{"value":null},"dimensions":{"value":"4 x 4 mm per array + pedestal","unit":"mm","confidence":"HIGH","source":"BrainGate publications","notes":"Same as Utah Array. Wireless variant adds small transmitter modules."},"wireless":{"value":"Custom 48 Mbps wireless (for wireless variant); wired pedestal (original)","unit":null,"confidence":"HIGH","source":"BrainGate publications","notes":null},"directionality":"read-only (recording)","batteryLife":{"value":36,"unit":"hours (wireless variant)","confidence":"HIGH","source":"BrainGate wireless BCI publication (ScienceDaily 2021)","notes":"Two wireless devices recorded at 48 Mbps from 200 electrodes with >36 hour battery life."},"electrodeMaterial":{"value":null},"electrodeImpedance":{"value":"100-800","unit":"kOhm","confidence":"MEDIUM","source":"Published BrainGate studies","notes":null},"adcResolution":{"value":null},"snr":{"value":null},"thermalBudget":{"value":null},"frequencyRange":{"value":null},"dataRate":{"value":48,"unit":"Mbps","confidence":"HIGH","source":"BrainGate wireless publication","notes":null},"targetRegions":[{"id":"m1","name":"Primary Motor Cortex","abbreviation":"M1","band":"N7","function":"Voluntary motor execution, somatotopic motor map (homunculus)"}],"qifBands":["N7"],"i0Depth":"I0-cortical","interfaceType":"read","threatCount":57,"topThreats":[{"id":"QIF-T0014","name":"Envelope modulation (stealth carrier)","severity":"high","niss":8.1,"category":"SI","status":"DEMONSTRATED"},{"id":"QIF-T0022","name":"Neurofeedback falsification","severity":"high","niss":8.1,"category":"DS","status":"EMERGING"},{"id":"QIF-T0055","name":"BCI cognitive warfare","severity":"critical","niss":8.1,"category":"PS","status":"THEORETICAL"},{"id":"QIF-T0065","name":"Algorithmic psychosis induction (recommendation weaponization)","severity":"critical","niss":8.1,"category":"CI","status":"CONFIRMED"},{"id":"QIF-T0033","name":"Identity erosion (long-term personality drift)","severity":"critical","niss":8,"category":"CI","status":"THEORETICAL"}],"threatsBySeverity":{"critical":19,"high":21,"medium":16,"low":1},"neurosecurityScore":{"overallScore":3.76,"severity":"Medium","subscores":{"CL":3.59,"MI":3.7,"MP":2.67,"PC":2.92,"EA":7.25},"vector":"NSv2.1:3.76/CL:3.59/MI:3.70/MP:2.67/PC:2.92/EA:7.25"}},{"id":"neuropace-rns","name":"RNS System (Responsive Neurostimulator)","manufacturer":"NeuroPace Inc.","deviceType":"Neuromodulation","deviceTypeLabel":"Neuromodulation","category":"invasive","fdaStatus":"PMA approved (P100026, 2013); next-gen launched","targetIndication":"Drug-resistant focal epilepsy","channels":{"value":4,"unit":"amplifier channels","confidence":"HIGH","source":"NeuroPace RNS System manual; FDA PMA P100026","notes":"Senses from up to 4 channels. Each lead has 4 electrode contacts. Two leads supported (8 contacts total, mapped to 4 amp channels)."},"samplingRate":{"value":null,"unit":"Hz","confidence":null,"source":null,"notes":"Internal sampling rate not publicly detailed in available sources."},"power":{"value":null},"dimensions":{"value":"60 x 27.5 x 7.5","unit":"mm","confidence":"HIGH","source":"NIH BRAIN Initiative device info sheet","notes":"Neurostimulator body. Implanted in cranial bone (craniectomy pocket)."},"wireless":{"value":"Proprietary near-field telemetry (wand-based)","unit":null,"confidence":"HIGH","source":"NeuroPace RNS System manual","notes":"Uses a programming wand held near the device for data download and programming. Not continuous wireless streaming."},"directionality":"bidirectional (sensing + responsive stimulation)","batteryLife":{"value":"8-10.8","unit":"years","confidence":"HIGH","source":"NeuroPace press release; FDA labeling","notes":"Next-gen: 8 years. Original model: up to 10.8 years at medium settings. Non-rechargeable."},"electrodeMaterial":{"value":"Platinum-iridium","unit":null,"confidence":"HIGH","source":"NeuroPace documentation; PMC review (PMC4598207)","notes":"4 contacts per lead, configurable as anode/cathode. Case can serve as cathode."},"electrodeImpedance":{"value":null,"unit":null,"confidence":"LOW","source":"PMC4598207","notes":"Impedance stabilizes after ~1 year. Constant-current design compensates for impedance changes."},"adcResolution":{"value":null},"snr":{"value":null},"thermalBudget":{"value":null},"frequencyRange":{"value":"ECoG bandwidth","unit":null,"confidence":"MEDIUM","source":"NeuroPace documentation","notes":"Records electrocorticographic (ECoG) patterns. Specific filter range not publicly detailed."},"dataRate":{"value":null},"targetRegions":[{"id":"hippocampus","name":"Hippocampus","abbreviation":"HIPP","band":"N6","function":"Episodic memory formation, spatial navigation, memory consolidation"},{"id":"bla","name":"Basolateral Amygdala","abbreviation":"BLA","band":"N6","function":"Fear conditioning, emotional valence assignment, associative learning. Cortical-like architecture."},{"id":"cerebellum_cortex","name":"Cerebellar Cortex","abbreviation":"CbCtx","band":"N3","function":"Motor coordination, error correction, timing, procedural learning. Purkinje cells are the sole output."}],"qifBands":["N6","N3"],"i0Depth":"I0-subcortical","interfaceType":"bidirectional","threatCount":63,"topThreats":[{"id":"QIF-T0122","name":"Chronic epileptogenic focus creation (kindling)","severity":"medium","niss":8.4,"category":"DS","status":"THEORETICAL"},{"id":"QIF-T0014","name":"Envelope modulation (stealth carrier)","severity":"high","niss":8.1,"category":"SI","status":"DEMONSTRATED"},{"id":"QIF-T0055","name":"BCI cognitive warfare","severity":"critical","niss":8.1,"category":"PS","status":"THEORETICAL"},{"id":"QIF-T0065","name":"Algorithmic psychosis induction (recommendation weaponization)","severity":"critical","niss":8.1,"category":"CI","status":"CONFIRMED"},{"id":"QIF-T0068","name":"Bifurcation forcing (critical parameter manipulation to trigger neural state transitions)","severity":"critical","niss":8.1,"category":"PS","status":"EMERGING"}],"threatsBySeverity":{"critical":20,"high":22,"medium":21,"low":0},"neurosecurityScore":null},{"id":"medtronic-percept-pc","name":"Percept PC Neurostimulator","manufacturer":"Medtronic plc","deviceType":"DBS","deviceTypeLabel":"Deep Brain Stimulation","category":"invasive","fdaStatus":"PMA approved (2020)","targetIndication":"Parkinson's disease, essential tremor, dystonia, epilepsy, OCD","channels":{"value":"2 (dual-channel IPG)","unit":"stimulation channels","confidence":"HIGH","source":"Medtronic product page; Expert Rev Med Devices 2021","notes":"Supports 1-2 DBS leads. BrainSense technology enables simultaneous sensing and stimulation."},"samplingRate":{"value":null},"power":{"value":null},"dimensions":{"value":"68 x 55 x 11","unit":"mm","confidence":"HIGH","source":"ResearchGate figure; Medtronic documentation","notes":"20% smaller and thinner than predecessor Activa PC."},"wireless":{"value":"Proprietary short-range telemetry (Bluetooth-based clinician programmer)","unit":null,"confidence":"MEDIUM","source":"Medtronic documentation","notes":null},"directionality":"bidirectional (stimulation + BrainSense LFP recording)","batteryLife":{"value":"~5 years estimated (non-rechargeable)","unit":"years","confidence":"MEDIUM","source":"Medtronic documentation; expert review","notes":"Silver vanadium oxide primary cell. BrainSense sensing shortens battery life. 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Major DBS target for dystonia."},{"id":"vim","name":"Ventral Intermediate Nucleus (Thalamus)","abbreviation":"VIM","band":"N4","function":"Cerebellar relay to motor cortex. Primary DBS target for essential tremor."}],"qifBands":["N5","N4"],"i0Depth":"I0-subcortical","interfaceType":"bidirectional","threatCount":33,"topThreats":[{"id":"QIF-T0014","name":"Envelope modulation (stealth carrier)","severity":"high","niss":8.1,"category":"SI","status":"DEMONSTRATED"},{"id":"QIF-T0055","name":"BCI cognitive warfare","severity":"critical","niss":8.1,"category":"PS","status":"THEORETICAL"},{"id":"QIF-T0065","name":"Algorithmic psychosis induction (recommendation weaponization)","severity":"critical","niss":8.1,"category":"CI","status":"CONFIRMED"},{"id":"QIF-T0068","name":"Bifurcation forcing (critical parameter manipulation to trigger neural state transitions)","severity":"critical","niss":8.1,"category":"PS","status":"EMERGING"},{"id":"QIF-T0009","name":"RF false brainwave injection","severity":"high","niss":7.4,"category":"SI","status":"EMERGING"}],"threatsBySeverity":{"critical":11,"high":9,"medium":12,"low":1},"neurosecurityScore":null},{"id":"medtronic-percept-rc","name":"Percept RC Neurostimulator","manufacturer":"Medtronic plc","deviceType":"DBS","deviceTypeLabel":"Deep Brain Stimulation","category":"invasive","fdaStatus":"PMA approved (Jan 2024)","targetIndication":"Same as Percept PC (Parkinson's, tremor, dystonia, epilepsy, OCD)","channels":{"value":"2 (dual-channel IPG)","unit":"stimulation channels","confidence":"HIGH","source":"Medtronic press release Jan 2024","notes":null},"samplingRate":{"value":null},"power":{"value":null},"dimensions":{"value":null},"wireless":{"value":null},"directionality":"bidirectional (stimulation + BrainSense LFP recording)","batteryLife":{"value":">15","unit":"years service life","confidence":"HIGH","source":"Medtronic press release Jan 2024","notes":"Rechargeable variant with consistent fast recharge."},"electrodeMaterial":{"value":null},"electrodeImpedance":{"value":null},"adcResolution":{"value":null},"snr":{"value":null},"thermalBudget":{"value":null},"frequencyRange":{"value":null},"dataRate":{"value":null},"targetRegions":[{"id":"stn","name":"Subthalamic Nucleus","abbreviation":"STN","band":"N5","function":"Hyperdirect pathway hub, motor urgency signal, stop-signal processing. 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Primary DBS target for essential tremor."}],"qifBands":["N5","N4"],"i0Depth":"I0-subcortical","interfaceType":"bidirectional","threatCount":33,"topThreats":[{"id":"QIF-T0014","name":"Envelope modulation (stealth carrier)","severity":"high","niss":8.1,"category":"SI","status":"DEMONSTRATED"},{"id":"QIF-T0055","name":"BCI cognitive warfare","severity":"critical","niss":8.1,"category":"PS","status":"THEORETICAL"},{"id":"QIF-T0065","name":"Algorithmic psychosis induction (recommendation weaponization)","severity":"critical","niss":8.1,"category":"CI","status":"CONFIRMED"},{"id":"QIF-T0068","name":"Bifurcation forcing (critical parameter manipulation to trigger neural state transitions)","severity":"critical","niss":8.1,"category":"PS","status":"EMERGING"},{"id":"QIF-T0009","name":"RF false brainwave injection","severity":"high","niss":7.4,"category":"SI","status":"EMERGING"}],"threatsBySeverity":{"critical":11,"high":9,"medium":12,"low":1},"neurosecurityScore":null},{"id":"synchron-stentrode","name":"Stentrode (with BrainPort transmitter)","manufacturer":"Synchron Inc.","deviceType":"invasive_endovascular","deviceTypeLabel":"Endovascular","category":"invasive","fdaStatus":"IDE approved; COMMAND trial ongoing (US)","targetIndication":"ALS, severe paralysis","channels":{"value":16,"unit":"electrodes","confidence":"HIGH","source":"Synchron technology page; JAMA Neurology publication (PMC9857731)","notes":"16-electrode sensing array on self-expanding nitinol stent."},"samplingRate":{"value":null,"unit":"kHz","confidence":null,"source":null,"notes":"Not publicly specified. Power gating can reduce sampling rates during idle."},"power":{"value":"<10","unit":"mW","confidence":"HIGH","source":"Auctores technical review of Stentrode system","notes":"Total power budget constrained to less than 10 mW. Power gating reduces idle consumption."},"dimensions":{"value":"Stent diameter matches vessel: 4-7 mm at superior sagittal sinus","unit":"mm","confidence":"HIGH","source":"JAMA Neurology publication; Synchron documentation","notes":"Stent-electrode array deployed endovascularly. BrainPort receiver implanted in chest."},"wireless":{"value":"Bluetooth Low Energy (BLE)","unit":null,"confidence":"HIGH","source":"Auctores technical review; Being Patient article","notes":"BrainPort chest unit transmits via BLE to external devices. Practical limit ~1 Mbps."},"directionality":"read-only (recording)","batteryLife":{"value":10,"unit":"years (design target)","confidence":"MEDIUM","source":"Auctores technical review","notes":"Wireless power via near-field RF to chest-mounted BrainPort unit. 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brain health assessment","channels":{"value":"2000+","unit":"optical channels","confidence":"HIGH","source":"PMC8765296 (J Biomed Opt 2022)","notes":"52 modules, each with 1 dual-wavelength laser source + 6 detectors = 312 source-detector pairs minimum. Cross-module channels add more."},"samplingRate":{"value":200,"unit":"Hz per detector","confidence":"HIGH","source":"PMC8765296","notes":null},"power":{"value":null},"dimensions":{"value":null},"wireless":{"value":null,"unit":null,"confidence":"MEDIUM","source":"PMC8765296; SPIE article","notes":"Wired (tethered to processing unit). Wearable but not wireless."},"directionality":"read-only (hemodynamic imaging)","batteryLife":{"value":null},"electrodeMaterial":{"value":null},"electrodeImpedance":{"value":null},"adcResolution":{"value":null},"snr":{"value":null},"thermalBudget":{"value":null},"frequencyRange":{"value":null},"dataRate":{"value":null},"targetRegions":[{"id":"pfc","name":"Prefrontal Cortex","abbreviation":"PFC","band":"N7","function":"Executive function, decision-making, working memory, personality, social behavior, planning"},{"id":"m1","name":"Primary Motor Cortex","abbreviation":"M1","band":"N7","function":"Voluntary motor execution, somatotopic motor map (homunculus)"},{"id":"ppc","name":"Posterior Parietal Cortex","abbreviation":"PPC","band":"N7","function":"Spatial awareness, sensorimotor integration, attention, reach planning"},{"id":"v1","name":"Primary Visual Cortex","abbreviation":"V1","band":"N7","function":"Primary visual processing — edge detection, orientation, spatial frequency, color"},{"id":"a1","name":"Primary Auditory Cortex","abbreviation":"A1","band":"N7","function":"Primary auditory processing — tonotopic frequency mapping, sound onset detection"}],"qifBands":["N7"],"i0Depth":"I0-noninvasive","interfaceType":"read","threatCount":57,"topThreats":[{"id":"QIF-T0014","name":"Envelope modulation (stealth carrier)","severity":"high","niss":8.1,"category":"SI","status":"DEMONSTRATED"},{"id":"QIF-T0022","name":"Neurofeedback falsification","severity":"high","niss":8.1,"category":"DS","status":"EMERGING"},{"id":"QIF-T0055","name":"BCI cognitive warfare","severity":"critical","niss":8.1,"category":"PS","status":"THEORETICAL"},{"id":"QIF-T0065","name":"Algorithmic psychosis induction (recommendation weaponization)","severity":"critical","niss":8.1,"category":"CI","status":"CONFIRMED"},{"id":"QIF-T0033","name":"Identity erosion (long-term personality drift)","severity":"critical","niss":8,"category":"CI","status":"THEORETICAL"}],"threatsBySeverity":{"critical":19,"high":21,"medium":16,"low":1},"neurosecurityScore":{"overallScore":2.93,"severity":"Low","subscores":{"CL":2.42,"MI":2.94,"MP":2.95,"PC":2.92,"EA":3.5},"vector":"NSv2.1:2.93/CL:2.42/MI:2.94/MP:2.95/PC:2.92/EA:3.50"}}],"stats":{"totalDevices":24,"manufacturers":19,"channelRange":{"min":4,"max":1024},"types":{"Cortical BCI":5,"Neuromodulation":1,"Deep Brain Stimulation":4,"Endovascular":1,"Cochlear Implant":2,"EEG (Consumer)":5,"EEG (Research Board)":2,"Invasive (Temporary)":1,"Minimally Invasive":1,"Non/Minimally Invasive":1,"fNIRS":1},"compiledDate":"2026-02-18"}},"brain_atlas":{"regions":[{"id":"pfc","name":"Prefrontal Cortex","abbreviation":"PFC","qif_band":"N7","parent_structure":"frontal_lobe","depth_class":"cortical","hemisphere":"bilateral","function":"Executive function, decision-making, working memory, personality, social behavior, planning","response_latency_ms":{"value":150,"range":[100,300],"measure":"Visual stimulus to PFC neuronal onset","notes":"Highly variable by task and stimulus type. Visual motion onset 40-60ms in frontal eye fields; decision-related activity 150-300ms.","citations":["Sugase-Miyamoto et al. 2008 (PNAS): PFC population activity 100-200ms","Romanski & Hwang 2012 (PMC3618972): VLPFC vocalization response shortest latency"],"confidence":"MEDIUM"},"oscillation_bands":["theta (4-8 Hz)","alpha (8-13 Hz)","beta (13-30 Hz)","gamma (30-100 Hz)"],"connections":["m1","broca","wernicke","hippocampus","bla","insula","acc","thalamus","striatum"],"allen_atlas_id":null,"brodmann_areas":[9,10,11,12,46,47],"how_it_works":"The PFC is the seat of executive function — the brain's CEO. It maintains information in working memory (holding a phone number in mind), inhibits impulses (not eating the cake), plans sequences (packing for a trip), and makes decisions by weighing options. It does this through sustained neural firing — PFC neurons can hold activity for seconds without ongoing input, unlike sensory neurons that fire only during stimulation. The PFC has extensive connections to every other cortical area and to subcortical structures (amygdala, basal ganglia, thalamus), making it the central hub for top-down control. It's the last brain region to fully myelinate, not completing until age ~25, which is why teenagers make impulsive decisions.","sub_structures":[{"name":"Dorsolateral PFC (dlPFC)","role":"Working memory, cognitive flexibility, planning. 'Cold' executive function."},{"name":"Ventromedial PFC (vmPFC)","role":"Decision-making, emotional regulation, value assessment. 'Hot' executive function."},{"name":"Orbitofrontal Cortex (OFC)","role":"Reward valuation, social behavior, impulse control. Damage → personality change (Phineas Gage)."},{"name":"Anterior PFC (Brodmann 10)","role":"Metacognition — thinking about thinking. Prospective memory, multitasking."},{"name":"Frontal Eye Fields (FEF)","role":"Voluntary eye movements (saccades). Visual attention direction."}],"processing_pipeline":"Sensory input (all modalities) → Posterior cortex → PFC integration → Working memory maintenance (persistent firing) → Decision computation (vmPFC value signals + dlPFC rule maintenance) → Motor output via PMC/SMA → M1 execution. Feedback loops: PFC ↔ Basal ganglia (action selection), PFC ↔ Amygdala (emotional regulation), PFC ↔ Hippocampus (memory retrieval).","signal_type":"Integrative — sustained firing patterns encoding rules, goals, and abstract representations","bci_relevance_detail":"PFC signals encode abstract intentions and decisions, making it valuable for high-level BCI control (selecting between options, error detection). However, PFC signals are noisier and more variable than motor cortex signals, making reliable decoding harder.","clinical_notes":"PFC damage → executive dysfunction: poor planning, impulsivity, flat affect, social inappropriateness. ADHD linked to PFC hypofunction (dopamine deficit in dlPFC). Depression linked to vmPFC/OFC dysfunction. Schizophrenia: dlPFC working memory deficits."},{"id":"m1","name":"Primary Motor Cortex","abbreviation":"M1","qif_band":"N7","parent_structure":"frontal_lobe","depth_class":"cortical","hemisphere":"bilateral","function":"Voluntary motor execution, somatotopic motor map (homunculus)","response_latency_ms":{"value":20,"range":[10,30],"measure":"Corticospinal conduction time to hand muscles","notes":"Central motor conduction time (CMCT) measured via TMS: ~20ms to hand. Cortical motor neurons activate ~100-150ms before movement onset.","citations":["Rossini et al. 2015: TMS guidelines, CMCT norms","Hallett 2007 (Neurology): TMS clinical neurophysiology"],"confidence":"HIGH"},"oscillation_bands":["beta (13-30 Hz)","gamma (30-100 Hz)","mu (8-12 Hz)"],"connections":["pfc","sma","pmc","s1_cortex","cerebellum_cortex","striatum","thalamus"],"allen_atlas_id":null,"brodmann_areas":[4],"how_it_works":"M1 contains the motor homunculus — a body map where each area controls specific muscles. Large pyramidal neurons (Betz cells) in Layer 5 send axons down the corticospinal tract to synapse on motor neurons in the spinal cord. The signal chain: PFC (decision to move) → SMA/PMC (motor planning, sequence) → M1 (execution command) → corticospinal tract → spinal motor neurons → neuromuscular junction → muscle contraction. M1 doesn't encode individual muscles in isolation — it encodes movement direction and force through population coding. Each neuron has a 'preferred direction' and the population vector determines actual movement direction.","sub_structures":[{"name":"Layer 5 (Betz cells)","role":"Giant pyramidal neurons. Direct corticospinal projection to spinal motor neurons."},{"name":"Motor homunculus","role":"Somatotopic body map. Hand/face get disproportionate cortical area."},{"name":"Hand knob","role":"Omega-shaped region for hand control. Primary target for motor BCIs."},{"name":"Face area","role":"Controls facial muscles, jaw, tongue, larynx."},{"name":"Leg area","role":"Medial surface. Controls hip, knee, ankle, foot."}],"processing_pipeline":"PFC (intention) → SMA (sequence planning) → PMC (spatial planning) → M1 Layer 5 (execution) → Corticospinal tract → Pyramidal decussation (crossing in medulla) → Lateral corticospinal tract → Spinal motor neuron → Neuromuscular junction (acetylcholine) → Muscle contraction","signal_type":"Motor — efferent electrical signals encoding movement direction and force","bci_relevance_detail":"Motor BCIs decode intended movement from M1 neural activity. Utah arrays in the hand knob area can decode individual finger movements. Population vector decoding: each neuron's firing rate × preferred direction, summed across population = decoded movement. BrainGate, Neuralink, and Blackrock focus here.","clinical_notes":"Stroke in M1 causes contralateral hemiparesis/hemiplegia. ALS progressively destroys motor neurons, making M1 a key BCI target (locked-in patients). Damage to left M1 affects right body and vice versa (pyramidal decussation)."},{"id":"v1","name":"Primary Visual Cortex","abbreviation":"V1","qif_band":"N7","parent_structure":"occipital_lobe","depth_class":"cortical","hemisphere":"bilateral","function":"Primary visual processing — edge detection, orientation, spatial frequency, color","response_latency_ms":{"value":56,"range":[56,100],"measure":"Visual stimulus to V1 ERP onset (C1 component)","notes":"C1 component (N75): 56-100ms post-stimulus. Mean onset ~56ms (earliest reported). Intracranial: category decoding by 100ms.","citations":["Clark et al. 1994 (Electroencephalogr Clin Neurophysiol): C1 mean onset 56ms","Di Russo et al. 2002 (PubMed 11797091): V1 source of C1 60-100ms","Liu et al. 2009 (J Neurosci): Object decoding from intracranial EEG at 100ms"],"confidence":"HIGH"},"oscillation_bands":["alpha (8-13 Hz)","gamma (30-100 Hz)"],"connections":["lgn_thalamus","v2_extrastriate","ppc"],"allen_atlas_id":null,"brodmann_areas":[17],"how_it_works":"Light enters the eye and hits the retina, where ~120 million rod cells (dim light) and ~6 million cone cells (color: red/green/blue) convert photons into electrical signals. These signals pass through retinal ganglion cells, travel via the optic nerve to the Lateral Geniculate Nucleus (LGN) in the thalamus, and then project to V1. V1 is organized into 6 layers. Layer 4 receives input from the LGN and contains simple cells that detect oriented edges at specific angles. Layers 2/3 combine these into complex cells that detect edges regardless of exact position. V1 creates a retinotopic map — neighboring points in the visual field map to neighboring neurons. The fovea (center of gaze) gets disproportionately more cortical area (cortical magnification).","sub_structures":[{"name":"Layer 4C","role":"Receives direct LGN input. Simple cells detect oriented edges."},{"name":"Layers 2/3","role":"Complex cells combine edge signals. Feed forward to V2."},{"name":"Layer 6","role":"Feedback to LGN, modulating incoming signals."},{"name":"Blob regions","role":"Color processing (cytochrome oxidase blobs). Wavelength-selective."},{"name":"Interblob regions","role":"Orientation and spatial frequency processing."},{"name":"Ocular dominance columns","role":"Alternating columns for left/right eye input. Basis of binocular vision."}],"processing_pipeline":"Photons → Retina (rods/cones) → Retinal ganglion cells → Optic nerve → Optic chiasm (partial crossing) → LGN (thalamus) → V1 Layer 4C → V1 Layers 2/3 → V2 → Ventral stream (V4 → IT: object recognition, 'what') + Dorsal stream (V3 → MT/V5 → PPC: motion/spatial, 'where')","signal_type":"Visual — electromagnetic radiation (380-740nm wavelength)","bci_relevance_detail":"Visual BCIs (cortical prostheses like Orion/PRIMA) stimulate V1 directly to create phosphenes. Resolution limited by electrode spacing vs. cortical magnification factor. Current devices produce ~600 phosphenes, far below the ~1 million needed for natural vision.","clinical_notes":"Damage to V1 causes cortical blindness. Damage to specific areas causes scotomas (blind spots). Lesions in ventral stream → visual agnosia (can't recognize objects). Lesions in dorsal stream → optic ataxia (can't reach for objects)."},{"id":"a1","name":"Primary Auditory Cortex","abbreviation":"A1","qif_band":"N7","parent_structure":"temporal_lobe","depth_class":"cortical","hemisphere":"bilateral","function":"Primary auditory processing — tonotopic frequency mapping, sound onset detection","response_latency_ms":{"value":40,"range":[25,80],"measure":"Auditory stimulus to cortical onset (Pa/high-gamma)","notes":"Middle-latency response Pa: 25-40ms. High-gamma (70-150 Hz) cortical response ~40ms. Full cortical processing: Na 12-27ms, Pa 25-40ms.","citations":["Liégeois-Chauvel et al. 1994 (J Neurosci): First-spike timing in auditory cortex","Nourski et al. 2022 (PMC9773383): Time-locked high-gamma responses at ~40ms","Picton et al. 2010 (PMC9443762): Middle-latency auditory response norms"],"confidence":"HIGH"},"oscillation_bands":["theta (4-8 Hz)","gamma (30-100 Hz)","high-gamma (70-150 Hz)"],"connections":["mgn_thalamus","wernicke","pfc","hippocampus"],"allen_atlas_id":null,"brodmann_areas":[41,42],"how_it_works":"Sound waves vibrate the eardrum (tympanic membrane), which transmits vibration through three tiny bones (malleus, incus, stapes) in the middle ear to the cochlea. Inside the cochlea, ~3,500 inner hair cells are arranged along the basilar membrane in a tonotopic gradient — high frequencies near the base, low frequencies at the apex. Hair cell deflection opens ion channels, generating electrical signals. These travel via the auditory nerve to the cochlear nucleus, then through the superior olive (sound localization) and inferior colliculus (integration) to the Medial Geniculate Nucleus (MGN) of the thalamus, and finally to A1. A1 maintains the tonotopic map: different frequencies activate different cortical columns.","sub_structures":[{"name":"Core (A1/R)","role":"Primary tonotopic map. Frequency-tuned neurons in columns."},{"name":"Belt areas","role":"Complex sound features — harmonics, spectral shape, bandwidth."},{"name":"Parabelt","role":"Higher-order processing. Feeds into ventral ('what') and dorsal ('where') auditory streams."},{"name":"Tonotopic columns","role":"Each column responds to a specific frequency. Low frequencies anterior, high frequencies posterior."}],"processing_pipeline":"Sound waves → Eardrum → Ossicles (malleus/incus/stapes) → Cochlea (hair cells on basilar membrane) → Auditory nerve → Cochlear nucleus → Superior olive (binaural localization) → Inferior colliculus → MGN (thalamus) → A1 → Belt → Parabelt → Ventral stream (sound identity) + Dorsal stream (sound location)","signal_type":"Auditory — mechanical pressure waves (20Hz-20kHz)","bci_relevance_detail":"Cochlear implants bypass damaged hair cells, directly stimulating the auditory nerve with ~22 electrode channels (vs. 3,500 hair cells). Auditory BCIs for brainstem-level deafness stimulate the cochlear nucleus directly. Current challenge: frequency resolution far below natural hearing.","clinical_notes":"Damage to A1 causes cortical deafness. Wernicke's area (adjacent) processes speech comprehension. Tinnitus linked to reorganization of tonotopic map after hearing loss — phantom signals from deafferented frequency regions."},{"id":"broca","name":"Broca's Area","abbreviation":"Broca","qif_band":"N7","parent_structure":"frontal_lobe","depth_class":"cortical","hemisphere":"left_dominant","function":"Speech production, syntactic processing, language planning","response_latency_ms":{"value":200,"range":[150,450],"measure":"Three sequential processing waves during linguistic tasks (human intracranial)","notes":"Unique three-wave pattern: ~200ms (lexical), ~320ms (grammatical), ~450ms (phonological) — Flinker et al. 2015, intracranial EEG. High-gamma (70-150 Hz) during speech production and perception.","citations":["Flinker et al. 2015 (PNAS): Three-wave pattern ~200/320/450ms in Broca's area","Sahin et al. 2009 (Science): Intracranial recordings lexical ~200ms, grammatical ~320ms, phonological ~450ms"],"confidence":"HIGH"},"oscillation_bands":["theta (4-8 Hz)","beta (13-30 Hz)","gamma (30-100 Hz)"],"connections":["pfc","wernicke","sma","m1","insula"],"allen_atlas_id":null,"brodmann_areas":[44,45],"how_it_works":"Broca's area is the brain's speech production center. It doesn't just control mouth muscles — it constructs the grammatical structure of sentences before you speak. When you want to say something, Wernicke's area formulates the meaning, then Broca's area sequences the phonemes, applies grammatical rules, and programs the precise motor movements of the tongue, lips, jaw, and larynx needed to produce speech. This motor programming is sent to the face/mouth region of M1 for execution. Broca's area also processes syntax when listening — it's activated by grammatically complex sentences ('the dog that the cat chased ran away') even when you're not speaking. It shows lateralization: left-hemisphere dominant in 95% of right-handed and 70% of left-handed people.","sub_structures":[{"name":"Pars opercularis (BA 44)","role":"Phonological processing and motor programming of speech. Articulatory planning."},{"name":"Pars triangularis (BA 45)","role":"Semantic and syntactic processing. Sentence comprehension and construction."},{"name":"Mirror neuron system","role":"Broca's area contains mirror neurons — fire both when performing an action and observing it. Basis for imitation learning and possibly language evolution."}],"processing_pipeline":"Conceptual intention (PFC) → Semantic formulation (Wernicke's area) → Arcuate fasciculus (white matter tract connecting Wernicke's → Broca's) → Grammatical structuring (BA 45) → Articulatory motor planning (BA 44) → M1 face/mouth area → Cranial nerves VII, X, XII → Lips, tongue, larynx, jaw → Speech output","signal_type":"Language production — syntactic structuring, articulatory motor programming","bci_relevance_detail":"Speech BCIs aim to decode speech intentions from Broca's area activity before motor execution. Recent work (Willett et al. 2023, Nature) decoded attempted speech at 62 words/min from a paralyzed participant by recording from the ventral premotor cortex near Broca's area. This is among the most promising BCI applications for locked-in patients.","clinical_notes":"Broca's aphasia: damage produces telegraphic speech — patient understands language but can't produce fluent sentences. Says 'want... coffee... now' instead of 'I would like some coffee please.' Comprehension relatively preserved. Frustration is common because the patient knows what they want to say but can't produce it."},{"id":"wernicke","name":"Wernicke's Area","abbreviation":"Wernicke","qif_band":"N7","parent_structure":"temporal_lobe","depth_class":"cortical","hemisphere":"left_dominant","function":"Language comprehension, semantic processing, speech reception","response_latency_ms":{"value":50,"range":[50,400],"measure":"Auditory speech to posterior STG high-gamma onset (human intracranial)","notes":"High-gamma onset in posterior STG: ~50-100ms (intracranial, Mesgarani & Chang 2012). N400 semantic component peaks ~400ms. MMN at ~100-200ms for deviant phonemes.","citations":["Mesgarani & Chang 2012 (Nature 485:233-236): STG speech response onset ~50ms","Kutas & Federmeier 2011 (Annu Rev Psychol): N400 review"],"confidence":"HIGH"},"oscillation_bands":["theta (4-8 Hz)","alpha (8-13 Hz)","gamma (30-100 Hz)"],"connections":["a1","broca","hippocampus","angular_gyrus"],"allen_atlas_id":null,"brodmann_areas":[22,39,40],"how_it_works":"Wernicke's area is the brain's language comprehension center — it extracts meaning from heard speech and read text. When you hear a sentence, the auditory cortex (A1) processes the raw sounds, then Wernicke's area maps those sounds onto stored word meanings (the mental lexicon). It performs phoneme-to-word mapping, semantic retrieval, and sentence-level comprehension. It connects to Broca's area via the arcuate fasciculus (a white matter highway), forming the language loop: Wernicke's decodes meaning → Broca's encodes speech → M1 produces speech. Wernicke's area also activates during reading (visual word form → semantic meaning) and inner speech (thinking in words).","sub_structures":[{"name":"Posterior superior temporal gyrus (BA 22)","role":"Core Wernicke's area. Phoneme discrimination, word recognition, sentence comprehension."},{"name":"Angular gyrus (BA 39)","role":"Cross-modal integration. Reading (visual → phonological → semantic). Semantic associations."},{"name":"Supramarginal gyrus (BA 40)","role":"Phonological processing. Sound-to-meaning mapping. Short-term verbal memory."}],"processing_pipeline":"Auditory input → A1 (raw sound) → Auditory association cortex (phoneme extraction) → Wernicke's area (word recognition, semantic retrieval) → Angular gyrus (cross-modal integration) → Arcuate fasciculus → Broca's area (if speaking). READING: Visual cortex → Visual Word Form Area (VWFA, fusiform gyrus) → Wernicke's area (meaning extraction).","signal_type":"Language comprehension — phoneme discrimination, semantic retrieval, sentence parsing","bci_relevance_detail":"Wernicke's area signals contain semantic content — the meaning behind words. Decoding semantic representations could enable BCIs that understand what the user is thinking about (not just motor intentions). Current research is in early stages; semantic decoding is harder than motor decoding.","clinical_notes":"Wernicke's aphasia: fluent but meaningless speech — patient speaks in grammatically correct but nonsensical sentences ('the slithy toves did gyre and gimble'). Cannot comprehend spoken or written language. Often unaware of their deficit (anosognosia). Conduction aphasia: arcuate fasciculus damage → can understand and speak but cannot repeat phrases."},{"id":"pmc","name":"Premotor Cortex","abbreviation":"PMC","qif_band":"N7","parent_structure":"frontal_lobe","depth_class":"cortical","hemisphere":"bilateral","function":"Motor planning, movement preparation, sensorimotor integration","response_latency_ms":{"value":120,"range":[80,200],"measure":"Visual cue to premotor neuron firing onset","notes":"Premotor neurons fire 80-200ms before movement onset. Visually guided reach: ~120ms.","citations":["Churchland et al. 2010 (Neuron): Preparatory activity in premotor cortex"],"confidence":"MEDIUM"},"oscillation_bands":["beta (13-30 Hz)","gamma (30-100 Hz)"],"connections":["m1","sma","pfc","ppc","cerebellum_cortex"],"allen_atlas_id":null,"brodmann_areas":[6],"how_it_works":"The premotor cortex plans WHERE in space a movement will go. While M1 executes the movement and SMA plans the sequence, PMC is responsible for selecting movements based on external cues — reaching for an object you see, ducking when something flies at you. It has two divisions: dorsal PMC (PMd) plans reaching movements guided by visual spatial information, and ventral PMC (PMv) plans grasping movements guided by object shape. PMv contains mirror neurons that fire both when grasping an object and when observing someone else grasp it, suggesting a role in understanding others' actions.","sub_structures":[{"name":"Dorsal premotor (PMd)","role":"Visually guided reaching. Spatial trajectory planning. Integrates visual target location with motor plan."},{"name":"Ventral premotor (PMv)","role":"Grasp planning. Object-oriented hand shaping. Mirror neuron system for action understanding."},{"name":"Mirror neurons (in PMv)","role":"Fire during both action execution and action observation. May underlie imitation, empathy, and language evolution."}],"processing_pipeline":"Visual target (PPC spatial processing) → PMd (reach trajectory computation) + Object recognition (ventral stream) → PMv (grasp configuration) → M1 (execution) → Corticospinal tract → Muscles. MIRROR SYSTEM: Observed action → PMv mirror neurons → Action understanding without executing.","signal_type":"Motor planning — spatial trajectory, grasp configuration, sensorimotor transformation","bci_relevance_detail":"PMC is a strong candidate for motor BCIs because it encodes movement intention before execution — giving the decoder a head start. PMd signals predict reach direction 100-200ms before M1 activation. Combined PMC+M1 recording improves BCI decode accuracy.","clinical_notes":"PMC lesions → ideomotor apraxia: knows what to do but can't translate intention to action. Can describe how to use a hammer but fumbles when trying. Mirror neuron dysfunction hypothesized in autism spectrum disorder (difficulty understanding others' intentions), though this remains debated."},{"id":"sma","name":"Supplementary Motor Area","abbreviation":"SMA","qif_band":"N7","parent_structure":"frontal_lobe","depth_class":"cortical","hemisphere":"bilateral","function":"Motor sequence planning, bimanual coordination, internally generated movements","response_latency_ms":{"value":500,"range":[300,1500],"measure":"Bereitschaftspotential (readiness potential) before movement","notes":"Readiness potential begins ~1500ms before voluntary movement onset. SMA proper activates ~500ms before movement.","citations":["Shibasaki & Hallett 2006 (Clin Neurophysiol): Bereitschaftspotential review"],"confidence":"MEDIUM"},"oscillation_bands":["beta (13-30 Hz)"],"connections":["m1","pmc","pfc","striatum","cerebellum_cortex"],"allen_atlas_id":null,"brodmann_areas":[6],"how_it_works":"The SMA plans WHEN and in what ORDER movements happen. It specializes in internally generated movement sequences — actions you initiate yourself rather than reactions to external stimuli. When a pianist plays a memorized piece from memory, the SMA is driving the sequence. It creates a motor program (the full plan) before execution begins. The 'readiness potential' — a slow brain wave that starts 1-2 seconds before a voluntary movement — originates primarily in the SMA. This signal represents the brain 'deciding' to move before you're consciously aware of the decision (Libet's famous experiment on free will).","sub_structures":[{"name":"SMA proper","role":"Motor sequence execution. Internally generated movements. Bimanual coordination."},{"name":"Pre-SMA","role":"Higher-level sequence planning. Decision to act. Switches between motor plans. Working memory for actions."}],"processing_pipeline":"Internal motor intention → Pre-SMA (action selection, sequence planning) → SMA proper (motor program assembly, timing) → M1 (execution). Readiness potential: Pre-SMA/SMA → builds activity 1-2 seconds before movement → Peaks at M1 activation → Movement onset. SEQUENCE LEARNING: initial (cortical/conscious) → practiced (SMA-basal ganglia loop) → automatic (cerebellar).","signal_type":"Motor sequencing — readiness potential, internally generated movement timing","bci_relevance_detail":"The readiness potential from SMA is one of the earliest detectable signals of movement intention, making it valuable for BCIs that need to anticipate movement. SMA activity is used in EEG-based BCIs for detecting self-initiated movement vs. rest states.","clinical_notes":"SMA lesions → motor neglect (failure to use contralateral limbs despite intact strength). Alien hand syndrome (medial frontal variant): hand performs purposeful but unintended actions. SMA seizures: bilateral asymmetric tonic posturing, fencing posture, preserved consciousness."},{"id":"ppc","name":"Posterior Parietal Cortex","abbreviation":"PPC","qif_band":"N7","parent_structure":"parietal_lobe","depth_class":"cortical","hemisphere":"bilateral","function":"Spatial awareness, sensorimotor integration, attention, reach planning","response_latency_ms":{"value":45,"range":[45,150],"measure":"Visual stimulus to LIP neuron onset (macaque single-unit); human intracranial ~60ms","notes":"Mean latency 45.2ms in macaque area LIP (Bisley et al. 2004). Human posterior IPS: ~60ms (Hertz et al. 2018, intracranial). P1 ERP component over parietal cortex: ~100ms.","citations":["Bisley et al. 2004 (J Neurosci 24(8):1833, DOI: 10.1523/JNEUROSCI.5007-03.2004): Mean latency 45.2ms","Hertz et al. 2018 (DOI: 10.1101/377887): Human intracranial ~60ms"],"confidence":"HIGH"},"oscillation_bands":["alpha (8-13 Hz)","beta (13-30 Hz)","gamma (30-100 Hz)"],"connections":["v1","m1","pmc","pfc","thalamus"],"allen_atlas_id":null,"brodmann_areas":[5,7,39,40],"how_it_works":"The PPC is the brain's spatial computing center — it builds and maintains a model of where things are in space relative to your body. It transforms sensory coordinates (where you SEE something) into motor coordinates (where to REACH for it). This sensorimotor transformation is critical for all goal-directed action. The PPC also processes attention — damage causes hemispatial neglect, where patients ignore one entire half of space (they eat food only from the right side of the plate, shave only the right side of the face). It integrates visual, somatosensory, and proprioceptive signals to create a unified spatial representation.","sub_structures":[{"name":"Superior parietal lobule (SPL/BA 5,7)","role":"Proprioceptive integration. Body schema. Reach planning. Updates limb position in space."},{"name":"Inferior parietal lobule (IPL/BA 39,40)","role":"Spatial attention. Multisensory integration. Damage → hemispatial neglect."},{"name":"Lateral intraparietal area (LIP)","role":"Eye movement planning (saccades). Visual attention map. Salience computation."},{"name":"Anterior intraparietal area (AIP)","role":"Object shape processing for grasping. Feeds PMv for grasp configuration."},{"name":"Medial intraparietal area (MIP)","role":"Reach target encoding. Visual-to-motor coordinate transformation for arm movements."}],"processing_pipeline":"Visual input (V1 → Dorsal stream) → PPC spatial map (MIP: reach targets, LIP: saccade targets, AIP: grasp targets) → Coordinate transformation (eye-centered → hand-centered) → PMC/M1 (execution). ATTENTION: Sensory input → IPL (salience detection) → Attentional spotlight → Enhanced processing of attended location → Biased competition in visual cortex.","signal_type":"Spatial computing — coordinate transformation, attentional selection, body schema","bci_relevance_detail":"PPC encodes movement intentions in a cognitive format (goals rather than muscle commands), making it attractive for high-level BCIs. PPC recordings can decode intended reach targets even in paralyzed patients who cannot actually reach. Andersen lab (Caltech) demonstrated thought-controlled cursor from PPC signals.","clinical_notes":"Right PPC damage → left hemispatial neglect (ignores left side of space — not blind, but unaware). Optic ataxia: can see objects but can't reach accurately for them (impaired visuomotor transformation). Balint syndrome (bilateral PPC): can only perceive one object at a time (simultanagnosia), can't direct gaze voluntarily."},{"id":"s1_cortex","name":"Primary Somatosensory Cortex","abbreviation":"S1","qif_band":"N7","parent_structure":"parietal_lobe","depth_class":"cortical","hemisphere":"bilateral","function":"Tactile sensation, proprioception, somatotopic sensory map (homunculus)","response_latency_ms":{"value":20,"range":[14,50],"measure":"Somatosensory evoked potential (SEP) N20 component","notes":"N20 component at ~20ms post-median nerve stimulus. Earliest cortical SEP. P14 (subcortical) at ~14ms.","citations":["Allison et al. 1991 (Cereb Cortex): Human cortical somatosensory evoked potentials","Cruccu et al. 2008 (Clin Neurophysiol): SEP guidelines"],"confidence":"HIGH"},"oscillation_bands":["mu (8-12 Hz)","beta (13-30 Hz)","gamma (30-100 Hz)"],"connections":["thalamus_vpl","m1","ppc","insula"],"allen_atlas_id":null,"brodmann_areas":[1,2,3],"how_it_works":"Touch, pressure, temperature, and pain receptors in the skin generate signals that travel through peripheral nerves to the spinal cord. There are 4 main receptor types: Meissner's corpuscles (light touch, texture), Merkel cells (pressure, fine detail), Pacinian corpuscles (deep pressure, vibration), and Ruffini endings (skin stretch). Signals ascend via the dorsal column-medial lemniscus pathway (touch/proprioception) or the spinothalamic tract (pain/temperature) to the ventral posterolateral nucleus (VPL) of the thalamus, then project to S1. S1 contains the sensory homunculus — a distorted body map where areas with high receptor density (fingers, lips, tongue) get more cortical space.","sub_structures":[{"name":"Brodmann Area 3a","role":"Proprioception from muscles and joints."},{"name":"Brodmann Area 3b","role":"Primary touch processing. Fine tactile discrimination."},{"name":"Brodmann Area 1","role":"Texture perception. Integrates across fingers."},{"name":"Brodmann Area 2","role":"Size, shape, and joint position. Object recognition by touch."},{"name":"Sensory homunculus","role":"Distorted body map. Fingers and lips have disproportionately large representation."}],"processing_pipeline":"Skin receptors (Meissner/Merkel/Pacinian/Ruffini) → Peripheral nerve → Dorsal root ganglion → Spinal cord dorsal column → Medulla (nucleus gracilis/cuneatus) → Medial lemniscus → VPL thalamus → S1 (Areas 3a/3b → 1 → 2) → S2 (bilateral integration) → PPC (spatial awareness)","signal_type":"Somatosensory — mechanical deformation, thermal, nociceptive","bci_relevance_detail":"Somatosensory BCIs provide artificial touch feedback for prosthetic limbs. Intracortical microstimulation of S1 can create naturalistic touch sensations. Key challenge: matching the receptor-type specificity (pressure vs. vibration vs. temperature) with electrical stimulation patterns.","clinical_notes":"Damage to S1 causes loss of fine touch discrimination (astereognosis). Phantom limb pain occurs from cortical reorganization — neighboring body map areas invade the deafferented region. The homunculus can reorganize after amputation within weeks."},{"id":"hippocampus","name":"Hippocampus","abbreviation":"HIPP","qif_band":"N6","parent_structure":"medial_temporal_lobe","depth_class":"subcortical","hemisphere":"bilateral","function":"Episodic memory formation, spatial navigation, memory consolidation","response_latency_ms":{"value":204,"range":[101,500],"measure":"Visual stimulus to hippocampal single-neuron response (human intracranial)","notes":"Median onset 204ms for visually responsive neurons (Mormann et al. 2008, human intracranial). Hierarchical: parahippocampal cortex ~101ms, entorhinal cortex ~206ms, hippocampus ~204ms. Sharp-wave ripples (SWR): 80-200 Hz, ~50-100ms duration.","citations":["Mormann, Kornblith, Quiroga et al. 2008 (J Neurosci 28(36):8865-8872, DOI: 10.1523/JNEUROSCI.1640-08.2008): Median onset 204ms","Buzsaki 2015 (Neuron): Sharp-wave ripples review"],"confidence":"HIGH"},"oscillation_bands":["theta (4-8 Hz)","gamma (30-100 Hz)","sharp-wave ripples (100-250 Hz)"],"connections":["entorhinal_cortex","bla","pfc","acc","thalamus"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The hippocampus is the brain's memory encoder — it converts short-term experiences into long-term memories through a process called consolidation. Information flows through a trisynaptic circuit: Entorhinal Cortex → Dentate Gyrus (pattern separation — making similar memories distinct) → CA3 (pattern completion — retrieving full memories from partial cues, autoassociative network) → CA1 (comparison and output) → back to Entorhinal Cortex. Long-term potentiation (LTP) — the strengthening of synapses through repeated activation — was first discovered here and is the cellular mechanism of learning. Place cells in the hippocampus fire at specific locations, creating a cognitive map of the environment (Nobel Prize, O'Keefe 2014).","sub_structures":[{"name":"Dentate Gyrus (DG)","role":"Pattern separation. Makes similar inputs distinguishable. One of only two brain areas with adult neurogenesis."},{"name":"CA3","role":"Autoassociative network. Pattern completion — retrieves full memory from partial cue. Recurrent connections."},{"name":"CA1","role":"Comparator — detects novelty by comparing CA3 output with entorhinal cortex input. Primary output layer."},{"name":"Subiculum","role":"Output relay to cortex, hypothalamus, and mammillary bodies."},{"name":"Entorhinal Cortex","role":"Grid cells (Nobel Prize, Moser & Moser 2014). Spatial coordinate system. Main input/output gateway."}],"processing_pipeline":"Experience → Sensory cortices → Perirhinal/Parahippocampal cortex → Entorhinal Cortex (grid cells) → DG (pattern separation) → CA3 (pattern completion) → CA1 (comparison) → Subiculum → Neocortex (long-term storage). Consolidation: during sleep (sharp-wave ripples), CA3 replays memories to neocortex for permanent storage.","signal_type":"Memory encoding — theta oscillations (4-8Hz) during encoding, sharp-wave ripples (80-120Hz) during consolidation","bci_relevance_detail":"Memory prostheses aim to restore hippocampal function by recording CA3 activity patterns and replaying them to CA1. DARPA RAM program demonstrated proof-of-concept in epilepsy patients. Closed-loop stimulation during theta can enhance memory encoding by ~15-25%.","clinical_notes":"Bilateral hippocampal damage → anterograde amnesia (can't form new memories; patient H.M.). Alzheimer's disease attacks the hippocampus first — early symptom is memory loss. Chronic stress shrinks hippocampal dendrites via cortisol. PTSD: hippocampus fails to contextualize fear memories, so they replay inappropriately."},{"id":"bla","name":"Basolateral Amygdala","abbreviation":"BLA","qif_band":"N6","parent_structure":"temporal_lobe","depth_class":"subcortical","hemisphere":"bilateral","function":"Fear conditioning, emotional valence assignment, associative learning. Cortical-like architecture.","response_latency_ms":{"value":74,"range":[74,200],"measure":"Visual threat stimulus to amygdala response (intracranial EEG)","notes":"Fastest documented: ~74ms via intracranial EEG (Mendez-Bertolo et al. 2016). NOT 12ms — no source exists for sub-20ms amygdala response. The 'fast path' bypasses thalamic gating but still requires ~74ms.","citations":["Mendez-Bertolo et al. 2016 (Nature Neuroscience): Amygdala response to fear at 74ms via intracranial EEG"],"confidence":"HIGH"},"oscillation_bands":["theta (4-8 Hz)","gamma (30-100 Hz)"],"connections":["hippocampus","pfc","acc","insula","cea","thalamus","v1"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The amygdala is the brain's threat detector and emotional significance tagger. The basolateral amygdala (BLA) receives sensory input from all modalities and evaluates it for emotional relevance — is this dangerous? rewarding? novel? It uses two pathways: a fast 'low road' (thalamus → amygdala, ~12ms) that triggers immediate defensive responses before conscious awareness, and a slow 'high road' (thalamus → cortex → amygdala, ~30-40ms) that provides detailed analysis. BLA neurons form fear associations through Pavlovian conditioning: a neutral stimulus paired with a threat creates a permanent synaptic strengthening, so the neutral stimulus alone triggers fear responses.","sub_structures":[{"name":"Lateral nucleus","role":"Sensory input gateway. Receives thalamic and cortical projections. Fear conditioning occurs here."},{"name":"Basal nucleus","role":"Integrates with PFC and hippocampus. Context-dependent emotional processing."},{"name":"Accessory basal nucleus","role":"Projects to ventral striatum. Links emotion to motivated behavior."}],"processing_pipeline":"Threat stimulus → Thalamus → LOW ROAD: direct to BLA lateral nucleus (12ms, crude but fast) + HIGH ROAD: via sensory cortex to BLA (30-40ms, detailed analysis) → Central Amygdala (CeA) → Hypothalamus (stress hormones) + Periaqueductal gray (freezing) + Nucleus basalis (arousal) + Locus coeruleus (norepinephrine) + Brainstem (startle reflex)","signal_type":"Emotional valence — rapid threat assessment, fear/reward learning signals","bci_relevance_detail":"DBS of the amygdala has been explored for treatment-resistant PTSD and anxiety. Key security concern: BCI signals near the amygdala could inadvertently trigger fear responses or suppress threat detection. Amygdala activity is a proposed biomarker for emotional state decoding in affective BCIs.","clinical_notes":"Bilateral amygdala damage → inability to recognize fear in faces, poor threat assessment (patient S.M.). PTSD: amygdala hyperactivation — threat detector stuck on high. Anxiety disorders: lowered amygdala activation threshold. Psychopathy: reduced amygdala response to distress cues."},{"id":"insula","name":"Insular Cortex","abbreviation":"Insula","qif_band":"N6","parent_structure":"lateral_sulcus","depth_class":"cortical","hemisphere":"bilateral","function":"Interoception, pain processing, emotional awareness, taste, autonomic regulation","response_latency_ms":{"value":200,"range":[200,350],"measure":"Auditory stimulus to insular HFA onset (human intracranial EEG)","notes":"Intracranial EEG: 200-350ms for auditory stimuli. Visual P300-like component in anterior insula: 250-338ms. Substantially slower than primary sensory cortices, consistent with higher-order integrative role.","citations":["Demarchi et al. 2019 (PMID: 31629197): Auditory deviance response in insula","Ibos et al. 2019 (DOI: 10.1007/s00429-019-01892-y): Visual oddball insular response"],"confidence":"MEDIUM"},"oscillation_bands":["theta (4-8 Hz)","alpha (8-13 Hz)","beta (13-30 Hz)"],"connections":["bla","acc","pfc","thalamus","s1_cortex","a1"],"allen_atlas_id":null,"brodmann_areas":[13,14,15,16],"how_it_works":"The insula is the brain's interoceptive cortex — it creates your sense of how your body feels from the inside. It maps internal body states: heart rate, gut feelings, muscle tension, temperature, itch, pain, hunger, thirst. The posterior insula receives raw interoceptive signals, and the anterior insula integrates them into a unified 'feeling state' — this is the neural basis of subjective emotional experience. When you feel anxious, the insula is registering elevated heart rate, shallow breathing, and muscle tension and constructing the feeling 'I am anxious.' It also processes disgust (both physical — rotten food — and moral — unfair behavior) and contains von Economo neurons for rapid intuitive judgments.","sub_structures":[{"name":"Posterior insula","role":"Primary interoceptive cortex. Receives visceral, thermal, and nociceptive input. Raw body signals."},{"name":"Anterior insula","role":"Integrates interoception with cognition and emotion. Subjective feeling states. Self-awareness."},{"name":"Von Economo neurons","role":"Rapid intuitive processing. Social emotions. Present in humans and great apes only."}],"processing_pipeline":"Internal body state (heart, gut, lungs, muscles) → Vagus nerve + spinal afferents → Brainstem nuclei (NTS, parabrachial) → Thalamus (VMpo, VMb) → Posterior insula (raw interoceptive map) → Anterior insula (integrated feeling state + prediction of future body states) → ACC/PFC (conscious awareness and decision-making based on body signals).","signal_type":"Interoceptive — internal body state mapping, subjective feeling construction","bci_relevance_detail":"Insular activity is a key target for affective BCIs — decoding emotional states from interoceptive processing. Insular stimulation during epilepsy surgery produces vivid visceral sensations (nausea, warmth, tingling). Security concern: disrupting insular processing could alter body awareness and emotional experience.","clinical_notes":"Insular stroke → loss of body awareness, inability to recognize emotions. Anterior insula damage → loss of disgust response, empathy deficits. Addiction: insula lesions can eliminate cigarette cravings instantly (suggesting insula maintains interoceptive urges). Anxiety disorders: insula hyperactivation amplifies body signals into threat."},{"id":"acc","name":"Anterior Cingulate Cortex","abbreviation":"ACC","qif_band":"N6","parent_structure":"cingulate_gyrus","depth_class":"cortical","hemisphere":"bilateral","function":"Conflict monitoring, error detection, pain processing, motivation, autonomic regulation","response_latency_ms":{"value":200,"range":[100,400],"measure":"ERN (Error-Related Negativity) onset","notes":"Error-related negativity (ERN) peaks ~80-150ms post-error. Conflict monitoring (N2): ~200-350ms. Pain processing: ~150-250ms.","citations":["Gehring et al. 2012 (Oxford Handbook): ERN and ACC"],"confidence":"MEDIUM"},"oscillation_bands":["theta (4-8 Hz)","alpha (8-13 Hz)"],"connections":["pfc","bla","hippocampus","insula","m1","striatum"],"allen_atlas_id":null,"brodmann_areas":[24,25,32,33],"how_it_works":"The ACC is the brain's conflict monitor and error detector. When you're doing a Stroop task (reading 'RED' written in blue ink), the ACC detects the conflict between reading and color-naming and signals the PFC to increase cognitive control. It fires when you make errors, when outcomes are worse than expected, and when you experience physical pain or social rejection (the same ACC regions activate for both — 'social pain' is neurologically real). The ACC integrates cognitive and emotional information, sitting at the intersection of the PFC (rational) and limbic (emotional) systems. It contains von Economo neurons (spindle cells) — large, fast-conducting neurons found only in humans and great apes, thought to enable rapid intuitive judgments.","sub_structures":[{"name":"Dorsal ACC (dACC)","role":"Cognitive division. Conflict monitoring, error detection, response selection. Activates when you need to override a habitual response."},{"name":"Ventral/Subgenual ACC (sgACC)","role":"Emotional division. Mood regulation, autonomic control. Hyperactive in depression — target for DBS."},{"name":"Von Economo neurons","role":"Large spindle-shaped neurons for rapid, intuitive decision-making. Found only in ACC and insula in humans/great apes."}],"processing_pipeline":"Response conflict (competing responses active simultaneously) → dACC detects conflict (ERN/theta oscillations at 200ms post-response) → Signals dlPFC to increase top-down control → dlPFC adjusts attention/inhibition → Reduced conflict on next trial. PAIN pathway: Nociceptive input → Thalamus → dACC (suffering/unpleasantness component, distinct from S1 sensory-discriminative component).","signal_type":"Conflict/error monitoring — theta oscillations (4-8Hz), error-related negativity (ERN)","bci_relevance_detail":"ACC signals can be used as error detection in BCIs — when the user's intended action doesn't match the decoded output, the ACC generates an error signal that can be used to correct the decoder in real-time. DBS of sgACC is an experimental treatment for treatment-resistant depression (Brodmann area 25 DBS).","clinical_notes":"Depression: sgACC hyperactivity (rumination, excessive self-monitoring). OCD: dACC hyperactivity (persistent error signal — 'something is wrong' feeling that won't resolve). Anterior cingulotomy (lesioning ACC) is a last-resort treatment for intractable OCD and chronic pain. Akinetic mutism: bilateral ACC damage → loss of motivation to speak or move despite intact motor ability."},{"id":"cingulate","name":"Cingulate Gyrus (Posterior)","abbreviation":"PCC","qif_band":"N6","parent_structure":"cingulate_gyrus","depth_class":"cortical","hemisphere":"bilateral","function":"Default mode network hub, self-referential processing, memory retrieval, spatial orientation","response_latency_ms":{"value":250,"range":[150,400],"measure":"Task-related deactivation/activation","notes":"Part of default mode network. Deactivation during task onset: ~200-300ms. Memory-related activation: variable.","citations":["Raichle 2015 (Neuron): Default mode network review"],"confidence":"LOW"},"oscillation_bands":["alpha (8-13 Hz)","theta (4-8 Hz)"],"connections":["hippocampus","pfc","acc","ppc","thalamus"],"allen_atlas_id":null,"brodmann_areas":[23,29,30,31],"how_it_works":"The posterior cingulate cortex (PCC) is a central hub of the default mode network (DMN) — the brain's resting-state network that activates when you're not focused on the external world. When your mind wanders, remembers the past, imagines the future, or thinks about yourself and others, the PCC is highly active. It deactivates sharply when you focus on an external task. The PCC integrates internal and external attention, acting as a switch between self-referential thought and focused engagement. It also plays a role in spatial memory, navigation, and assessing the personal significance of stimuli.","sub_structures":[{"name":"Posterior cingulate cortex (PCC)","role":"Default mode network hub. Self-referential thought, mind-wandering, autobiographical memory."},{"name":"Retrosplenial cortex","role":"Spatial memory and navigation. Translates between egocentric (body-relative) and allocentric (world-relative) coordinates."},{"name":"Precuneus","role":"Self-awareness, episodic memory retrieval, visuospatial processing. One of the most metabolically active brain regions at rest."}],"processing_pipeline":"Rest/mind-wandering → PCC activates (DMN hub) → Integrates hippocampus (memory), mPFC (self-reference), angular gyrus (semantic) → Internal mentation. TASK ONSET: External stimulus → PCC deactivates → Dorsal attention network activates → Focused processing. NAVIGATION: Spatial cues → Retrosplenial cortex (coordinate transformation) → Hippocampus (cognitive map) → PPC (spatial planning).","signal_type":"Default mode — self-referential processing, internal mentation, attention switching","bci_relevance_detail":"PCC/DMN deactivation is a reliable neural marker of attention and engagement — useful for BCIs that monitor cognitive state. Neurofeedback targeting PCC activity has been explored for meditation training and ADHD. Disruption of DMN is associated with psychedelic states and some psychiatric conditions.","clinical_notes":"PCC is among the first regions affected in Alzheimer's disease — shows hypometabolism on PET years before symptoms. Damage to retrosplenial cortex → topographical disorientation (lost in familiar places). The precuneus is one of the last brain regions to lose activity under anesthesia and one of the first to recover — possibly related to consciousness itself."},{"id":"striatum","name":"Striatum (Caudate + Putamen)","abbreviation":"Striatum","qif_band":"N5","parent_structure":"basal_ganglia","depth_class":"subcortical","hemisphere":"bilateral","function":"Motor selection, habit learning, reward prediction, action initiation","response_latency_ms":{"value":100,"range":[60,300],"measure":"Reward-related or movement-related striatal firing","notes":"Reward prediction error: ~100ms post-outcome. Movement-related: ~60-200ms before movement in putamen. Caudate: 100-300ms for cognitive tasks.","citations":["Schultz 2006 (Annu Rev Psychol): Reward signals in basal ganglia"],"confidence":"MEDIUM"},"oscillation_bands":["beta (13-30 Hz)","gamma (30-100 Hz)"],"connections":["pfc","m1","sma","acc","gpi","gpe","stn","substantia_nigra","thalamus"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The striatum is the input station of the basal ganglia — the brain's action selection system. It works like a voting machine: cortical areas 'propose' actions by sending glutamate signals to the striatum, and the striatum selects which action to execute by disinhibiting the appropriate thalamic pathway. It uses two opposing pathways: the Direct pathway (D1 receptors, dopamine excites → GO signal, facilitates selected action) and the Indirect pathway (D2 receptors, dopamine inhibits → STOP signal, suppresses competing actions). Dopamine from the substantia nigra tips the balance: more dopamine → more action initiation. This is why Parkinson's (dopamine loss) causes inability to initiate movement, and why cocaine/amphetamine (dopamine excess) causes hyperactivity.","sub_structures":[{"name":"Caudate nucleus","role":"Cognitive and eye movement circuits. Goal-directed behavior. Learning stimulus-response associations."},{"name":"Putamen","role":"Sensorimotor circuits. Habitual/automatic movement. Most affected in Parkinson's disease."},{"name":"Nucleus Accumbens (NAc)","role":"Ventral striatum. Reward processing, motivation, addiction. 'Pleasure center.' Receives dopamine from VTA."},{"name":"Medium spiny neurons (MSNs)","role":"95% of striatal neurons. D1-MSNs (direct pathway, GO) and D2-MSNs (indirect pathway, STOP)."}],"processing_pipeline":"Cortex (action proposals via glutamate) → Striatum (MSNs integrate cortical votes) → DIRECT: D1-MSNs → inhibit GPi → disinhibit thalamus → cortex → GO. INDIRECT: D2-MSNs → inhibit GPe → disinhibit STN → excite GPi → inhibit thalamus → STOP. Dopamine from SNc/VTA modulates the balance: D1 activation (GO) + D2 inhibition (less STOP) = net facilitation of movement.","signal_type":"Action selection — integration of cortical inputs, dopamine-modulated output via direct/indirect pathways","bci_relevance_detail":"DBS of the striatum (specifically STN or GPi) is the primary treatment for advanced Parkinson's disease. Closed-loop DBS systems detect pathological beta oscillations (13-30Hz) in the striatum and deliver stimulation only when needed. Security: disrupting striatal dopamine signaling could alter reward processing (addiction) or motor control (dyskinesia).","clinical_notes":"Parkinson's disease: loss of dopamine neurons in SNc → striatal dopamine depletion → inability to initiate movement (bradykinesia), rigidity, tremor. Huntington's disease: degeneration of striatal MSNs → involuntary movements (chorea) + cognitive decline. OCD: hyperactive caudate → repetitive action loops. Addiction: hijacked NAc reward circuitry."},{"id":"gpi","name":"Globus Pallidus Internus","abbreviation":"GPi","qif_band":"N5","parent_structure":"basal_ganglia","depth_class":"subcortical","hemisphere":"bilateral","function":"Primary output nucleus of basal ganglia (inhibitory). Major DBS target for dystonia.","response_latency_ms":{"value":15,"range":[10,30],"measure":"Striatal input to GPi output latency","notes":"Direct pathway: striatum → GPi ~10-15ms. GPi tonic firing rate: 60-80 Hz. Pause latency during movement: ~15ms.","citations":["Kita & Kita 2011 (J Neurosci): Basal ganglia circuit timing"],"confidence":"MEDIUM"},"oscillation_bands":["beta (13-30 Hz)"],"connections":["striatum","stn","thalamus","gpe"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The GPi is the primary output nucleus of the basal ganglia — it acts as the final gate between the striatum's action selection and the thalamus. In its resting state, the GPi tonically inhibits the thalamus, preventing all movements. When the direct pathway (D1-MSNs from striatum) inhibits specific GPi neurons, those thalamic targets are released from inhibition — allowing the selected movement to proceed while all other movements remain suppressed. This 'release from inhibition' mechanism (double negative = positive) is how the basal ganglia select one action from many competing options.","sub_structures":[{"name":"Motor territory","role":"Receives from putamen. Controls voluntary movement via thalamus (VA/VL) → M1/SMA."},{"name":"Associative territory","role":"Receives from caudate. Cognitive action selection via thalamus → PFC."},{"name":"Limbic territory","role":"Receives from ventral striatum/NAc. Emotional/motivational action selection."}],"processing_pipeline":"RESTING STATE: GPi → tonic GABA inhibition → Thalamus silenced → No movement. DIRECT PATHWAY: Cortex → Striatum (D1-MSNs) → INHIBITS GPi → Thalamus released → Cortex activated → Selected movement executes. INDIRECT PATHWAY: Cortex → Striatum (D2-MSNs) → GPe → STN → EXCITES GPi → Thalamus more inhibited → Competing movements suppressed.","signal_type":"Inhibitory gate — tonic GABAergic inhibition of thalamus, released by striatal direct pathway","bci_relevance_detail":"DBS of the GPi is FDA-approved for Parkinson's disease (reduces rigidity and dyskinesia) and primary generalized dystonia. GPi-DBS works by disrupting pathological oscillatory patterns rather than simply inhibiting or exciting — the mechanism is still debated.","clinical_notes":"Parkinson's: GPi hyperactive (excess inhibition of thalamus → bradykinesia). Dystonia: GPi firing patterns become irregular → sustained muscle contractions. Hemiballismus: damage to STN → GPi loses excitatory input → thalamus disinhibited → wild flinging movements of contralateral limbs."},{"id":"gpe","name":"Globus Pallidus Externus","abbreviation":"GPe","qif_band":"N5","parent_structure":"basal_ganglia","depth_class":"subcortical","hemisphere":"bilateral","function":"Indirect pathway relay, tonic inhibition of STN, basal ganglia modulation","response_latency_ms":{"value":15,"range":[10,25],"measure":"Striatal input to GPe relay","notes":"Similar timing to GPi. Tonic firing rate: 50-70 Hz. Indirect pathway involves additional STN relay.","citations":["Kita & Kita 2011 (J Neurosci): Basal ganglia circuit timing"],"confidence":"MEDIUM"},"oscillation_bands":["beta (13-30 Hz)"],"connections":["striatum","stn","gpi","thalamus"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The GPe is a critical relay in the indirect pathway of the basal ganglia. It normally inhibits the STN, keeping it in check. When the indirect pathway is activated (D2-MSNs from striatum inhibit GPe), the GPe stops inhibiting the STN, which then excites the GPi, which increases thalamic inhibition — resulting in movement suppression. The GPe also has direct connections to the striatum (the 'arkypallidal' pathway, discovered recently), suggesting it plays a more complex role than simple relay — it may actively reshape striatal activity patterns and contribute to action cancellation (stopping a movement mid-execution).","sub_structures":[{"name":"Prototypic neurons (~70%)","role":"Project to STN and GPi. Classic indirect pathway relay. Provide tonic inhibition of STN."},{"name":"Arkypallidal neurons (~25%)","role":"Project BACK to striatum. Recently discovered. May enable rapid action cancellation by resetting striatal activity."},{"name":"Cortically-projecting neurons (~5%)","role":"Direct GPe → cortex projection (discovered 2015). Function still being characterized."}],"processing_pipeline":"INDIRECT PATHWAY: Cortex → Striatum (D2-MSNs) → INHIBIT GPe prototypic neurons → STN released from inhibition → STN excites GPi → GPi increases thalamic inhibition → Movement suppressed. ARKYPALLIDAL: GPe arkypallidal neurons → Back to striatum → Reset ongoing activity → Action cancellation (stop signal).","signal_type":"Inhibitory relay — GABAergic modulation of STN, striatal feedback via arkypallidal projection","bci_relevance_detail":"GPe is not a common DBS target but is increasingly recognized as important for understanding basal ganglia computation. GPe-STN circuit dynamics generate pathological beta oscillations in Parkinson's — understanding this could improve closed-loop DBS algorithms.","clinical_notes":"GPe lesions → poorly characterized in isolation (rare). GPe dysfunction contributes to Parkinson's pathophysiology — loss of dopamine shifts balance toward indirect pathway dominance → GPe hypoactive → STN hyperactive → excessive movement suppression."},{"id":"stn","name":"Subthalamic Nucleus","abbreviation":"STN","qif_band":"N5","parent_structure":"basal_ganglia","depth_class":"subcortical","hemisphere":"bilateral","function":"Hyperdirect pathway hub, motor urgency signal, stop-signal processing. Primary DBS target for Parkinson's.","response_latency_ms":{"value":5,"range":[5,70],"measure":"Cortical stimulation to STN response (antidromic/orthodromic)","notes":"Short-latency (antidromic via hyperdirect pathway): 5-10ms. Medium-latency (antidromic cortical): 2-15ms. Long-latency (orthodromic polysynaptic): 20-70ms. Beta oscillations (15-30 Hz) pathological in PD.","citations":["Nambu et al. 2002 (J Neurophysiol): Hyperdirect pathway timing","Miocinovic et al. 2018 (JAMA Neurol): STN DBS mechanisms","Walker et al. 2012 (PMC2852267): Short-latency visual input to STN via superior colliculus"],"confidence":"HIGH"},"oscillation_bands":["beta (13-30 Hz)","theta (4-8 Hz)"],"connections":["gpe","gpi","substantia_nigra","cortex_hyperdirect","thalamus"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The STN is the only excitatory (glutamatergic) nucleus in the basal ganglia — everything else uses GABA. It acts as an emergency brake on movement. When activated, it broadly excites the GPi, which increases inhibition of the thalamus, suppressing all motor output. This 'hyperdirect' pathway (cortex → STN → GPi) is faster than the direct or indirect pathways, allowing rapid cancellation of all movements before the slower selection pathways can choose an action. This is why you can freeze mid-reach when you notice something wrong. In Parkinson's disease, the STN becomes hyperactive (excessive braking), contributing to the difficulty initiating movement.","sub_structures":[{"name":"Motor territory (dorsolateral)","role":"Receives from M1/PMC. The DBS target for Parkinson's disease."},{"name":"Associative territory (ventromedial)","role":"Receives from PFC. Cognitive control, decision conflict."},{"name":"Limbic territory (medial tip)","role":"Receives from ACC/OFC. Emotional impulsivity. DBS here → impulsive behavior side effect."}],"processing_pipeline":"HYPERDIRECT PATHWAY (fastest, ~10ms): Cortex → STN (glutamate, excitatory) → GPi (broadly excited) → Thalamus (broadly inhibited) → ALL movements paused. Then DIRECT pathway (slower) releases the selected action. This creates a 'pause-then-select' mechanism: brake everything first, then release the intended movement. IN PARKINSON'S: dopamine loss → D2 indirect pathway overactive → GPe inhibited → STN hyperactive → excessive GPi activation → thalamus over-inhibited → bradykinesia.","signal_type":"Excitatory brake — glutamatergic broad-stop signal, hyperdirect pathway terminus","bci_relevance_detail":"STN-DBS is the gold standard surgical treatment for Parkinson's disease. High-frequency stimulation (~130Hz) disrupts pathological beta oscillations. Adaptive/closed-loop DBS detects beta power in real-time and stimulates only when needed — reduces side effects and extends battery life. The STN is the most studied DBS target in neuroscience.","clinical_notes":"STN-DBS for Parkinson's: reduces tremor, rigidity, and bradykinesia. Allows L-DOPA dose reduction by ~50%. Side effects: speech difficulty, impulsivity (limbic territory stimulation), mood changes. STN lesion → hemiballismus (violent flinging movements — loss of the 'brake'). Adaptive DBS (Medtronic Percept PC) now available clinically."},{"id":"substantia_nigra","name":"Substantia Nigra","abbreviation":"SNr/SNc","qif_band":"N5","parent_structure":"midbrain","depth_class":"subcortical","hemisphere":"bilateral","function":"Dopamine production (SNc, pars compacta) and basal ganglia output (SNr, pars reticulata). Degeneration causes Parkinson's.","response_latency_ms":{"value":20,"range":[10,50],"measure":"Dopamine burst latency to unexpected reward","notes":"SNc dopamine neurons: burst response to unexpected reward ~10-20ms. SNr inhibitory output timing similar to GPi.","citations":["Schultz 1998 (J Neurophysiol): Dopamine neuron temporal dynamics"],"confidence":"MEDIUM"},"oscillation_bands":["beta (13-30 Hz)"],"connections":["striatum","stn","gpi","thalamus"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The substantia nigra ('black substance,' named for its dark neuromelanin pigment) contains dopamine-producing neurons that are critical for movement initiation. The pars compacta (SNc) produces dopamine that modulates the striatum's direct/indirect pathways. Each SNc neuron branches extensively, with a single neuron innervating up to 75,000 striatal neurons. The pars reticulata (SNr) is an output nucleus of the basal ganglia, sending GABAergic inhibition to the thalamus and superior colliculus. The SNc neurons fire in two modes: tonic (steady, background dopamine for normal function) and phasic bursts (reward prediction error signals — fire when reward is unexpected, pause when expected reward is absent).","sub_structures":[{"name":"Pars Compacta (SNc)","role":"Dopamine production. Projects to dorsal striatum. Loss of these neurons → Parkinson's disease."},{"name":"Pars Reticulata (SNr)","role":"GABAergic output nucleus. Inhibits thalamus and superior colliculus. Functionally similar to GPi."},{"name":"Neuromelanin","role":"Dark pigment (dopamine metabolite). Accumulates with age. Lost in Parkinson's — visible on MRI as depigmentation."}],"processing_pipeline":"SNc dopamine neurons → nigrostriatal pathway → Dorsal striatum (caudate/putamen). Firing pattern: TONIC (~4Hz baseline) maintains normal dopamine tone for movement. PHASIC BURST (reward prediction error): unexpected reward → burst → increased dopamine → reinforces preceding action. Expected reward absent → pause → decreased dopamine → weakens preceding action. This is the cellular mechanism of reinforcement learning.","signal_type":"Dopaminergic — tonic baseline for motor function, phasic bursts for reward prediction error","bci_relevance_detail":"Loss of SNc neurons is the defining pathology of Parkinson's. DBS downstream (STN/GPi) compensates but doesn't restore dopamine. Cell replacement therapies and optogenetic stimulation of remaining SNc neurons are active research areas. BCI security: disrupting SNc output mimics Parkinson's symptoms.","clinical_notes":"Parkinson's disease: >60% SNc neuron loss before symptoms appear. Progressive — no cure, DBS and L-DOPA are symptomatic treatments. L-DOPA is converted to dopamine by remaining neurons, compensating for loss. Over time, fewer neurons → L-DOPA dose increases → dyskinesia (involuntary movements from excess dopamine)."},{"id":"vta","name":"Ventral Tegmental Area","abbreviation":"VTA","qif_band":"N5","parent_structure":"midbrain","depth_class":"subcortical","hemisphere":"bilateral","function":"Dopamine production — origin of mesolimbic and mesocortical pathways. Primary reward and motivation center.","primary_function":"Dopaminergic neurotransmission, reward processing","response_latency_ms":{"value":100,"range":[50,200],"measure":"VTA dopamine neuron burst latency to reward cue","notes":"VTA dopamine neurons show phasic burst firing 50-200ms after reward-predictive cue. Longer latency than SNc due to mesolimbic projection targets.","citations":["Schultz 1998 (J Neurophysiol): Dopamine neuron temporal dynamics","Cohen et al. 2012 (Nature): VTA dopamine neuron reward prediction error signals"],"confidence":"MEDIUM"},"oscillation_bands":["theta (4-8 Hz)","gamma (30-100 Hz)"],"connections":["striatum","pfc","bla","hippocampus","acc"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The VTA is the origin of the brain's reward and motivation system. Its dopamine neurons project to the nucleus accumbens (mesolimbic pathway — reward, motivation, addiction) and prefrontal cortex (mesocortical pathway — working memory, executive function). VTA neurons encode reward prediction errors: they burst-fire when something unexpectedly good happens and pause when an expected reward doesn't arrive. This signal teaches the brain what actions lead to reward. Every addictive substance increases dopamine release from VTA neurons — cocaine blocks reuptake, amphetamine reverses the transporter, opioids disinhibit VTA neurons by suppressing local GABA interneurons.","sub_structures":[{"name":"Dopamine neurons (~60%)","role":"Project to NAc (mesolimbic) and PFC (mesocortical). Encode reward prediction error."},{"name":"GABA neurons (~35%)","role":"Local inhibitory interneurons. Regulate dopamine neuron firing. Opioids suppress these → disinhibit dopamine release."},{"name":"Glutamate neurons (~5%)","role":"Co-release glutamate with dopamine. Modulate excitatory/inhibitory balance in target areas."}],"processing_pipeline":"Reward stimulus → Lateral hypothalamus/PFC/amygdala → VTA dopamine neurons → MESOLIMBIC: VTA → NAc (motivation, wanting, reward salience) → PFC (decision-making about reward). MESOCORTICAL: VTA → PFC (working memory, cognitive control). Reward prediction error: Expected reward present → no change. Unexpected reward → burst → dopamine surge → 'this was good, do it again.' Expected reward absent → pause → dopamine dip → 'this was bad, avoid.'","signal_type":"Dopaminergic — reward prediction error, motivation, incentive salience","bci_relevance_detail":"VTA is the target of the mesolimbic pathway — the primary reward circuit. DBS of the VTA has been explored for treatment-resistant depression. Security concern: VTA stimulation could create artificial reward signals, potentially inducing compulsive device use or addiction-like states. This is one of the most ethically sensitive BCI targets.","clinical_notes":"Depression: VTA hypoactivity → reduced motivation and anhedonia. Addiction: VTA hijacked by drugs → natural rewards become insufficient. Schizophrenia (positive symptoms): excessive VTA dopamine in mesolimbic pathway. ADHD: insufficient VTA dopamine in mesocortical pathway to PFC."},{"id":"thalamus","name":"Thalamus","abbreviation":"Thal","qif_band":"N4","parent_structure":"diencephalon","depth_class":"subcortical","hemisphere":"bilateral","function":"Central relay station for all sensory modalities (except olfaction). Gating via reticular thalamic nucleus (TRN) implements default-deny on ascending traffic.","response_latency_ms":{"value":4,"range":[3,20],"measure":"LGN relay latency (retina → LGN → V1)","notes":"LGN visual relay: ~4.2ms (retinogeniculate delay). VPL somatosensory relay: ~14ms (median nerve). MGN auditory relay: ~6-8ms. TRN gating adds ~2-5ms.","citations":["Sherman & Guillery 2006 (Exploring the Thalamus and Its Role in Cortical Function): Thalamic relay timing","Briggs & Usrey 2011 (Neuron): LGN response properties"],"confidence":"HIGH"},"oscillation_bands":["alpha (8-13 Hz)","sleep spindles (11-15 Hz)","delta (0.5-4 Hz)"],"connections":["all_cortical_areas","striatum","cerebellum_nuclei","reticular_formation","hypothalamus"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The thalamus is the brain's central relay station — nearly ALL sensory information (except smell) passes through it before reaching the cortex. But it's not a passive relay. The thalamus actively gates what reaches conscious awareness by switching between two modes: tonic mode (steady firing, passes information faithfully) and burst mode (rhythmic firing, blocks information — this happens during sleep and absence seizures). Each sensory modality has its own thalamic nucleus: LGN for vision, MGN for hearing, VPL/VPM for touch. The thalamus also receives massive feedback from the cortex (10x more cortical→thalamic connections than thalamic→cortical), making it a dynamic filter that the cortex uses to control its own input.","sub_structures":[{"name":"Lateral Geniculate Nucleus (LGN)","role":"Visual relay. 6 layers: magnocellular (motion) and parvocellular (detail/color). Receives retinal ganglion cell axons."},{"name":"Medial Geniculate Nucleus (MGN)","role":"Auditory relay. Tonotopic organization preserved from cochlea."},{"name":"Ventral Posterolateral (VPL)","role":"Somatosensory relay for body. Touch, pressure, proprioception from medial lemniscus."},{"name":"Ventral Posteromedial (VPM)","role":"Somatosensory relay for face. Trigeminal nerve input."},{"name":"Pulvinar","role":"Largest thalamic nucleus. Visual attention, multisensory integration. Directs cortical attention."},{"name":"Reticular nucleus","role":"Surrounds the thalamus like a shell. GABAergic — inhibits other thalamic nuclei. Controls the 'gate' of consciousness."}],"processing_pipeline":"Sensory receptors → Sensory nerves → Specific thalamic nucleus (LGN/MGN/VPL) → Thalamocortical radiations → Layer 4 of corresponding cortical area. Gating mechanism: Reticular nucleus receives cortical feedback → inhibits thalamic relay neurons → blocks sensory input from reaching cortex (selective attention). During sleep: thalamic neurons enter burst mode → thalamocortical oscillations → sleep spindles (12-15Hz) = memory consolidation.","signal_type":"Relay and gating — tonic mode (faithful transmission) vs. burst mode (rhythmic blocking)","bci_relevance_detail":"DBS of the thalamus (VIM nucleus) is FDA-approved for essential tremor. Thalamic stimulation can modulate consciousness level in minimally conscious patients. The thalamic gate is a critical security concern: disrupting thalamic relay could alter all conscious sensory experience simultaneously.","clinical_notes":"Thalamic stroke → thalamic pain syndrome (severe, chronic pain from damaged sensory relay). Absence seizures: thalamocortical circuits enter pathological burst oscillation (3Hz spike-and-wave). Fatal familial insomnia: prion disease destroying thalamus → complete inability to sleep → death."},{"id":"hypothalamus","name":"Hypothalamus","abbreviation":"HyTh","qif_band":"N4","parent_structure":"diencephalon","depth_class":"subcortical","hemisphere":"bilateral","function":"Homeostatic regulation — temperature, hunger, thirst, circadian rhythm, hormone release, autonomic output","response_latency_ms":{"value":7,"range":[7,20],"measure":"Lateral hypothalamus evoked potential to sciatic nerve stimulation (cat)","notes":"Lateral hypothalamus: ~7ms (cat, evoked potential). Acoustic/somatosensory: <15-20ms across mammals. Homeostatic outputs operate on seconds-to-hours timescale. CAUTION: 7ms value from animal data — human intracranial data extremely limited.","citations":["Yamamoto et al. 2020 (Physiol Behav, DOI: 10.1016/j.physbeh.2020.113100): Fast sensory responses in lateral hypothalamus review"],"confidence":"LOW"},"oscillation_bands":["slow oscillations (<1 Hz)","theta (4-8 Hz)"],"connections":["thalamus","bla","brainstem_autonomic","pituitary"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The hypothalamus is the brain's master regulator of homeostasis — it keeps your body alive by controlling temperature, hunger, thirst, sleep/wake cycles, hormones, and the autonomic nervous system. Despite being only ~4 grams (about the size of an almond), it controls the entire endocrine system through the pituitary gland. It works by sensing blood chemistry directly (glucose, osmolarity, temperature, hormones) and adjusting outputs to maintain set points. When you're cold, the hypothalamus triggers shivering and vasoconstriction. When you're dehydrated, it releases ADH to retain water. The suprachiasmatic nucleus (SCN) is the master circadian clock, entrained by light via the retinohypothalamic tract.","sub_structures":[{"name":"Suprachiasmatic Nucleus (SCN)","role":"Master circadian clock. ~20,000 neurons with autonomous 24-hour firing rhythms. Entrained by retinal light input."},{"name":"Paraventricular Nucleus (PVN)","role":"Stress response (CRH → pituitary → ACTH → cortisol). Also produces oxytocin and vasopressin."},{"name":"Lateral Hypothalamus","role":"Hunger/feeding center. Contains orexin/hypocretin neurons (loss → narcolepsy)."},{"name":"Ventromedial Hypothalamus","role":"Satiety center. Damage → hyperphagia (uncontrollable eating)."},{"name":"Preoptic Area","role":"Thermoregulation and sleep promotion (VLPO sleep switch)."}],"processing_pipeline":"Blood chemistry sensors (glucose, osmolarity, temperature, hormones) → Hypothalamic nuclei → TWO output pathways: (1) Endocrine: hypothalamus → releasing hormones → pituitary gland → target endocrine glands (thyroid, adrenal, gonads) → systemic hormones. (2) Autonomic: hypothalamus → brainstem autonomic centers → sympathetic/parasympathetic nervous system → heart rate, blood pressure, digestion, pupils.","signal_type":"Homeostatic — chemical sensing and neuroendocrine/autonomic output","bci_relevance_detail":"DBS of the hypothalamus has been explored for cluster headaches (posterior hypothalamus) and obesity (ventromedial). Security concern: hypothalamic disruption via BCI could alter fundamental homeostatic set points — body temperature, hunger, sleep-wake cycles, stress response. This represents a particularly dangerous attack surface because effects are systemic and potentially life-threatening.","clinical_notes":"Hypothalamic tumors → precocious puberty, diabetes insipidus, thermoregulation failure. Narcolepsy: loss of orexin neurons in lateral hypothalamus. Cushing's disease: excess CRH from PVN → chronic cortisol elevation."},{"id":"vim","name":"Ventral Intermediate Nucleus (Thalamus)","abbreviation":"VIM","qif_band":"N4","parent_structure":"thalamus","depth_class":"subcortical","hemisphere":"bilateral","function":"Cerebellar relay to motor cortex. Primary DBS target for essential tremor.","response_latency_ms":{"value":8,"range":[5,15],"measure":"Cerebellothalamic conduction time","notes":"Dentate nucleus to VIM: ~5-8ms. VIM to motor cortex: ~3-5ms. Total cerebellar loop: ~20-40ms.","citations":["Middleton & Strick 2000 (Brain Res Rev): Basal ganglia and cerebellar loops"],"confidence":"MEDIUM"},"oscillation_bands":["tremor frequency (4-6 Hz)","beta (13-30 Hz)"],"connections":["cerebellum_nuclei","m1","pmc","sma"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The VIM is a specific nucleus within the thalamus that relays cerebellar output to the motor cortex. Cerebellar signals carrying movement corrections pass through the VIM on their way to M1 and PMC. The VIM plays a critical role in tremor because pathological oscillations in the cerebello-thalamo-cortical loop are amplified through this nucleus. In essential tremor (the most common movement disorder, affecting ~5% of people over 65), the VIM becomes locked in rhythmic oscillatory firing at 4-12Hz, which drives the characteristic action tremor.","sub_structures":[{"name":"VIM proper","role":"Cerebellar relay to motor cortex. Target for tremor DBS and focused ultrasound (FUS)."},{"name":"Adjacent VOp (ventral oral posterior)","role":"Pallidal relay. Receives from GPi. Sends to SMA/PMC. Sometimes included in DBS targeting."}],"processing_pipeline":"Cerebellum (deep nuclei) → Superior cerebellar peduncle → Crosses midline (decussation) → VIM thalamus → Thalamocortical projections → M1/PMC (motor correction applied). IN TREMOR: pathological oscillation in cerebellum ↔ VIM ↔ cortex loop → rhythmic motor output → visible tremor. DBS at 130Hz disrupts this oscillation.","signal_type":"Motor relay — cerebellar correction signals to cortex, pathological tremor oscillations when dysfunctional","bci_relevance_detail":"VIM-DBS is the most established DBS target (FDA-approved 1997 for essential tremor). VIM-targeted focused ultrasound (Insightec Exablate Neuro) provides lesion-free tremor treatment. Closed-loop VIM stimulation triggered by tremor detection is an active research area.","clinical_notes":"Essential tremor: VIM-DBS reduces tremor by 60-90%. Focused ultrasound thalamotomy (FUS): non-invasive, one-session treatment, FDA-approved 2016. Risk: VIM is small (~4mm) and adjacent to sensory thalamus — miss by 1-2mm → numbness or paresthesias."},{"id":"ant","name":"Anterior Nucleus of Thalamus","abbreviation":"ANT","qif_band":"N4","parent_structure":"thalamus","depth_class":"subcortical","hemisphere":"bilateral","function":"Limbic relay, memory circuits (Papez circuit). DBS target for drug-resistant epilepsy.","response_latency_ms":{"value":10,"range":[5,20],"measure":"Hippocampal input relay time","notes":"Mammillothalamic tract conduction to ANT: ~5-10ms. ANT to cingulate cortex: ~5-10ms.","citations":["Fisher et al. 2010 (Epilepsia): SANTE trial, ANT DBS for epilepsy"],"confidence":"LOW"},"oscillation_bands":["theta (4-8 Hz)"],"connections":["hippocampus","mammillary_bodies","cingulate","pfc"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The ANT is a thalamic nucleus that sits at the crossroads of the limbic system's memory circuit (the Papez circuit). It relays information from the hippocampus (via the mammillary bodies and mammillothalamic tract) to the cingulate cortex, creating a loop critical for episodic memory and spatial navigation. The ANT contains head direction cells — neurons that fire when the animal faces a specific direction, regardless of location. These work alongside hippocampal place cells and entorhinal grid cells to create the brain's internal GPS.","sub_structures":[{"name":"Anteroventral (AV)","role":"Head direction cell cluster. Spatial orientation signaling."},{"name":"Anteromedial (AM)","role":"Memory processing. Mammillothalamic tract input."},{"name":"Anterodorsal (AD)","role":"Dense head direction cell population. Vestibular integration for directional sense."}],"processing_pipeline":"Hippocampus (memory encoding) → Fornix → Mammillary bodies → Mammillothalamic tract → ANT → Cingulate cortex (PCC) → Parahippocampal gyrus → back to Hippocampus (Papez circuit complete). HEAD DIRECTION: Vestibular input → Brainstem → Lateral mammillary nucleus → ANT (AD) → Retrosplenial cortex → Spatial orientation signal.","signal_type":"Limbic relay — memory circuit (Papez circuit), head direction signals","bci_relevance_detail":"ANT-DBS is FDA-approved for drug-resistant epilepsy (SANTE trial, Medtronic). Stimulation modulates seizure propagation through the limbic circuit. The ANT is also being explored as a target for memory enhancement — stimulation during specific memory phases may strengthen encoding.","clinical_notes":"ANT-DBS reduces seizure frequency by ~40-70% in drug-resistant epilepsy. Korsakoff syndrome (thiamine deficiency, often from alcoholism): mammillary body/ANT damage → severe anterograde amnesia and confabulation. ANT lesions in animal models impair spatial memory and navigation."},{"id":"cerebellum_cortex","name":"Cerebellar Cortex","abbreviation":"CbCtx","qif_band":"N3","parent_structure":"cerebellum","depth_class":"infratentorial","hemisphere":"bilateral","function":"Motor coordination, error correction, timing, procedural learning. Purkinje cells are the sole output.","response_latency_ms":{"value":20,"range":[10,50],"measure":"Mossy fiber input to Purkinje cell response","notes":"Climbing fiber input: ~10ms. Mossy fiber → granule cell → parallel fiber → Purkinje cell: ~15-25ms. Complex spike latency: ~10ms from IO input. Purkinje cells fire at 30-100 Hz tonic.","citations":["Heck et al. 2013 (PubMed 23970855): Purkinje cell rebound timing","Johansson et al. 2014 (PNAS): Memory trace timing in Purkinje cells"],"confidence":"MEDIUM"},"oscillation_bands":["gamma (30-100 Hz)"],"connections":["cerebellum_nuclei","pons_nuclei","inferior_olive","m1_via_thalamus"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The cerebellum contains more neurons than the rest of the brain combined (~69 billion vs. ~17 billion in cerebral cortex) yet takes up only 10% of brain volume. It's the brain's real-time error correction system for movement. The cerebellar cortex has a remarkably uniform circuit: mossy fibers carry motor commands from the cortex (via pons), climbing fibers carry error signals from the inferior olive, and Purkinje cells (the sole output) integrate both to compute corrections. The circuit works as a forward model — it predicts the sensory consequences of a movement and compares the prediction with actual sensory feedback. The difference (error signal) updates the model via long-term depression at the parallel fiber-Purkinje cell synapse. This is why you get better at throwing darts with practice — the cerebellum is continuously reducing error.","sub_structures":[{"name":"Molecular layer","role":"Contains parallel fibers (granule cell axons) and Purkinje cell dendrites. Computation happens here."},{"name":"Purkinje cell layer","role":"Single row of Purkinje cells — the sole output. Largest neurons in the brain. Each receives ~200,000 parallel fiber inputs."},{"name":"Granular layer","role":"Most densely packed neurons in the brain. Granule cells receive mossy fiber input, send parallel fibers up."},{"name":"Climbing fibers","role":"From inferior olive. Carry error signals. One climbing fiber per Purkinje cell. Triggers complex spikes."},{"name":"Mossy fibers","role":"From pontine nuclei (cortical motor commands) and spinal cord (proprioception). Divergent input to granule cells."}],"processing_pipeline":"Motor cortex (planned movement) → Pontine nuclei → Mossy fibers → Granule cells → Parallel fibers → Purkinje cells (prediction). SIMULTANEOUSLY: Actual movement → Sensory feedback → Inferior olive → Climbing fibers → Purkinje cells (error signal). MISMATCH: climbing fiber error → long-term depression at parallel fiber synapse → updated prediction → smoother movement next time. Output: Purkinje cells (inhibitory) → Deep cerebellar nuclei → Thalamus → Motor cortex (corrected motor commands).","signal_type":"Error correction — forward model prediction vs. sensory feedback comparison","bci_relevance_detail":"Cerebellar stimulation (tDCS/TMS) can enhance motor learning speed. Cerebellar BCIs could augment coordination in real-time. Less explored than cortical BCIs but the uniform circuit architecture makes it theoretically tractable.","clinical_notes":"Cerebellar damage → ataxia (uncoordinated movement), dysmetria (overshooting targets), intention tremor, slurred speech (dysarthria). Cerebellar cognitive affective syndrome: damage to posterior cerebellum causes executive dysfunction, spatial cognition deficits, personality changes — showing the cerebellum does more than just motor control."},{"id":"cerebellum_nuclei","name":"Deep Cerebellar Nuclei","abbreviation":"DCN","qif_band":"N3","parent_structure":"cerebellum","depth_class":"infratentorial","hemisphere":"bilateral","function":"Cerebellar output nuclei (dentate, interposed, fastigial). Relay to thalamus and brainstem.","response_latency_ms":{"value":15,"range":[5,30],"measure":"Purkinje cell inhibition to nuclear rebound firing","notes":"Rebound firing after Purkinje cell pause: ~15ms. Dentate nucleus to thalamus: ~5-8ms.","citations":["Heck et al. 2013 (PubMed 23970855): Rebound activity in cerebellar nuclei"],"confidence":"MEDIUM"},"oscillation_bands":["beta (13-30 Hz)"],"connections":["cerebellum_cortex","vim","thalamus","reticular_formation"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The deep cerebellar nuclei (DCN) are the cerebellum's output stations — almost all cerebellar output passes through them. Purkinje cells in the cerebellar cortex are inhibitory (GABAergic), so they work by modulating the tonic excitatory output of the DCN. When a Purkinje cell fires, it suppresses its target DCN neuron; when the Purkinje cell pauses, the DCN neuron fires and sends corrective signals to the motor system. This 'release from inhibition' mechanism allows precise timing of motor corrections — the pause in Purkinje firing IS the correction signal.","sub_structures":[{"name":"Dentate nucleus","role":"Largest DCN. Projects to thalamus → motor and prefrontal cortex. Involved in motor planning AND cognition."},{"name":"Interposed nuclei (emboliform + globose)","role":"Project to red nucleus → rubrospinal tract. Limb movement corrections. Error-based motor learning."},{"name":"Fastigial nucleus","role":"Projects to vestibular nuclei and reticular formation. Balance, posture, eye movements, autonomic function."}],"processing_pipeline":"Cerebellar cortex (Purkinje cells, inhibitory) → DCN (release from inhibition = corrective signal) → DENTATE: Thalamus (VL) → M1/PMC/PFC (motor correction and cognitive modulation). INTERPOSED: Red nucleus → Rubrospinal tract → Spinal motor neurons (limb correction). FASTIGIAL: Vestibular nuclei → Balance reflexes; Reticular formation → Posture; Autonomic centers → Heart rate, blood pressure.","signal_type":"Motor output — corrective signals timed by Purkinje cell inhibition patterns","bci_relevance_detail":"DCN stimulation is being explored for motor rehabilitation after stroke — amplifying cerebellar corrective signals to help relearn movement. The dentate nucleus's connections to PFC suggest cerebellar neuromodulation could enhance cognitive functions beyond motor control.","clinical_notes":"Dentate lesions → intention tremor (tremor that worsens as you approach a target) + dysmetria (overshooting). Fastigial lesions → truncal ataxia (inability to sit/stand without swaying). Cerebellar cognitive affective syndrome: DCN damage affecting cognitive circuits → executive dysfunction, personality changes."},{"id":"vermis","name":"Cerebellar Vermis","abbreviation":"Vermis","qif_band":"N3","parent_structure":"cerebellum","depth_class":"infratentorial","hemisphere":"midline","function":"Axial motor control, balance, gait, vestibular integration, emotional regulation","response_latency_ms":{"value":20,"range":[10,40],"measure":"Vestibular input to vermis response","notes":"Vestibulocerebellar projections: ~10-20ms. Vermis involved in both motor and emotional processing (Schmahmann dysmetria of thought).","citations":["Schmahmann 2019 (Cerebellum): Cerebellar cognitive affective syndrome"],"confidence":"LOW"},"oscillation_bands":["theta (4-8 Hz)"],"connections":["vestibular_nuclei","fastigial_nucleus","reticular_formation","spinal_cord"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The cerebellar vermis (Latin for 'worm') is the midline structure of the cerebellum that controls trunk/axial muscles, balance, and posture. While the lateral cerebellar hemispheres handle limb coordination, the vermis keeps your body upright and stable. It receives vestibular input (balance signals from the inner ear), proprioceptive input from trunk muscles, and visual motion signals. The vermis is also increasingly recognized for its role in emotional regulation — it has connections to limbic structures, and vermis abnormalities are found in autism, schizophrenia, and mood disorders.","sub_structures":[{"name":"Anterior vermis (lobules I-V)","role":"Spinal input. Trunk and proximal limb coordination. Postural reflexes."},{"name":"Posterior vermis (lobules VI-IX)","role":"Vestibular and visual input. Balance, gaze stabilization. Emotional regulation via fastigial nucleus → limbic."},{"name":"Flocculonodular lobe (lobule X)","role":"Vestibulocerebellum. Vestibulo-ocular reflex (VOR). Balance during head movement."}],"processing_pipeline":"Vestibular organs (semicircular canals, otoliths) → Vestibular nuclei → Vermis (balance computation) → Fastigial nucleus → Vestibular nuclei (VOR correction) + Reticular formation (postural tone) + Spinal cord (balance reflexes). EMOTIONAL: Vermis → Fastigial nucleus → Hypothalamus/Amygdala (emotional modulation).","signal_type":"Balance and axial motor — vestibular integration, postural control, emotional modulation","bci_relevance_detail":"Vermis stimulation (tDCS/TMS) is being explored for ataxia rehabilitation and emotional regulation. Less relevant for typical motor BCIs but important for vestibular prostheses and balance-related applications.","clinical_notes":"Vermis lesions → truncal ataxia (wide-based staggering gait, like being drunk). Medulloblastoma (childhood brain tumor) commonly arises in the vermis → balance problems as presenting symptom. Vermis hypoplasia found in some cases of autism — may contribute to motor clumsiness and emotional dysregulation."},{"id":"medulla","name":"Medulla Oblongata","abbreviation":"Med","qif_band":"N2","parent_structure":"brainstem","depth_class":"infratentorial","hemisphere":"midline","function":"Vital autonomic centers — respiratory rhythm, cardiac center, blood pressure (vasomotor), vomiting reflex","response_latency_ms":{"value":2,"range":[1,4],"measure":"BAEP Wave II (cochlear nuclei in rostral medulla)","notes":"Brainstem auditory evoked potential Wave II: 2-4ms post-stimulus. Cochlear nuclei generate Wave II. Medullary autonomic reflexes: 5-20ms.","citations":["NCBI Bookshelf NBK597358: Brainstem auditory evoked response norms","Hall 2007 (New Handbook of Auditory Evoked Responses): BAEP wave generators"],"confidence":"HIGH"},"oscillation_bands":["respiratory rhythm (~0.2-0.5 Hz)","cardiac rhythm (~1 Hz)"],"connections":["pons","spinal_cord","vermis","thalamus","hypothalamus"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The medulla oblongata is the most vital brain structure — it keeps you alive by controlling breathing, heart rate, and blood pressure automatically. It contains the cardiovascular center (adjusts heart rate and blood vessel diameter), the respiratory center (generates the rhythm of breathing — you don't have to think about breathing because the medulla does it), and the vomiting center. All motor and sensory pathways between the brain and spinal cord pass through the medulla. The pyramidal decussation occurs here — where 90% of corticospinal motor fibers cross from one side to the other, which is why the left brain controls the right body. Cranial nerves IX (glossopharyngeal), X (vagus), XI (accessory), and XII (hypoglossal) originate here.","sub_structures":[{"name":"Cardiovascular center","role":"Controls heart rate (vagus nerve → slow, sympathetic → fast) and blood vessel tone. Baroreceptor reflex."},{"name":"Respiratory center","role":"Pre-Bötzinger complex generates breathing rhythm (~12-20 breaths/min). Chemoreceptors detect CO₂/pH."},{"name":"Area postrema","role":"Vomiting center. Outside blood-brain barrier — can detect toxins in blood directly."},{"name":"Nucleus tractus solitarius (NTS)","role":"Receives ALL visceral sensory input (vagus nerve). Integrates taste, blood pressure, respiration, gut signals."},{"name":"Pyramidal decussation","role":"Where corticospinal tract crosses midline. 90% of motor fibers cross here."}],"processing_pipeline":"BREATHING: Pre-Bötzinger complex (pacemaker neurons) → Phrenic nerve → Diaphragm contraction → Inspiration. Chemoreceptors detect high CO₂ → increase respiratory rate. HEART: Baroreceptors (carotid/aortic) detect blood pressure → NTS → Cardiovascular center → Vagus nerve (parasympathetic, slows heart) or sympathetic nerves (speeds heart). ALL MOTOR: Cortex → Internal capsule → Cerebral peduncle → Pyramidal decussation (crossing) → Lateral corticospinal tract → Spinal motor neurons.","signal_type":"Autonomic — rhythmic pattern generation for vital functions, visceral sensory integration","bci_relevance_detail":"Vagus nerve stimulation (VNS) — an FDA-approved neuromodulation therapy — targets medullary circuits for epilepsy and depression. The medulla is generally avoided in invasive BCI placement due to risk of respiratory/cardiac arrest. Security: any BCI disruption reaching the medulla could be life-threatening.","clinical_notes":"Medullary stroke → life-threatening: respiratory failure, cardiac arrest, or Wallenberg syndrome (lateral medullary syndrome — difficulty swallowing, vertigo, loss of pain/temperature sensation on one side). Brainstem death = death — when the medulla stops functioning, there is no spontaneous breathing or heartbeat."},{"id":"pons","name":"Pons","abbreviation":"Pons","qif_band":"N2","parent_structure":"brainstem","depth_class":"infratentorial","hemisphere":"midline","function":"Relay between cortex and cerebellum, REM sleep regulation, auditory relay (superior olivary complex)","response_latency_ms":{"value":4,"range":[3,5],"measure":"BAEP Wave III (superior olivary complex in caudal pons)","notes":"BAEP Wave III: 3-5ms. Superior olivary complex generates Wave III. Pontine nuclei relay cortical signals to cerebellum.","citations":["NCBI Bookshelf NBK597358: BAEP wave generators","Wikipedia: Brainstem auditory evoked potential"],"confidence":"HIGH"},"oscillation_bands":["pontine waves (PGO spikes during REM)"],"connections":["medulla","midbrain","cerebellum_cortex","thalamus"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The pons (Latin for 'bridge') connects the cerebral cortex to the cerebellum and relays signals between upper and lower brain structures. It plays a critical role in sleep regulation — the pontine reticular formation generates REM sleep by activating the cortex (dreams) while simultaneously paralyzing voluntary muscles (REM atonia) to prevent acting out dreams. The locus coeruleus, located in the pons, is the brain's primary source of norepinephrine — it fires rapidly during alertness and stress (fight-or-flight), moderately during waking, and goes silent during REM sleep. The pons also contains nuclei for cranial nerves V (trigeminal — facial sensation), VI (abducens — lateral eye movement), VII (facial — facial expression), and VIII (vestibulocochlear — hearing and balance).","sub_structures":[{"name":"Locus coeruleus","role":"Primary norepinephrine source. ~50,000 neurons project throughout entire brain. Alertness, attention, stress response, fight-or-flight."},{"name":"Pontine reticular formation","role":"REM sleep generation. Activates cortex for dreaming while triggering muscle atonia."},{"name":"Pontine nuclei","role":"Relay cortical motor commands to cerebellum via middle cerebellar peduncle. Critical for motor learning."},{"name":"Raphe nuclei (pontine)","role":"Serotonin production. Mood, sleep-wake, pain modulation. Antidepressants (SSRIs) target this system."},{"name":"Parabrachial nucleus","role":"Relays visceral/taste/pain signals to forebrain. Involved in breathing control and arousal."}],"processing_pipeline":"SLEEP: Pontine reticular formation → Acetylcholine release → Cortical activation (dreams) + Glycine/GABA to spinal motor neurons → Muscle paralysis (REM atonia). AROUSAL: Locus coeruleus → Norepinephrine to entire cortex → Increased alertness, attention, vigilance. MOTOR RELAY: Cortex → Pontine nuclei → Middle cerebellar peduncle → Cerebellar cortex (motor command copy for error correction).","signal_type":"Modulatory — noradrenergic arousal, serotonergic mood regulation, cholinergic REM generation","bci_relevance_detail":"Locus coeruleus-norepinephrine system modulates attention and arousal globally — important for BCI performance (alertness affects signal quality). Pontine DBS has been explored for disorders of consciousness. Security: disrupting pontine sleep circuits could cause REM behavior disorder (acting out dreams) or narcolepsy-like episodes.","clinical_notes":"Pontine stroke → locked-in syndrome (conscious but completely paralyzed except eye movements — basis for BCI communication). REM behavior disorder: loss of REM atonia → violent dream enactment, often precedes Parkinson's by years. Central pontine myelinolysis: rapid sodium correction → demyelination → quadriplegia."},{"id":"midbrain","name":"Midbrain (Mesencephalon)","abbreviation":"MB","qif_band":"N2","parent_structure":"brainstem","depth_class":"infratentorial","hemisphere":"midline","function":"Visual/auditory reflexes (superior/inferior colliculi), dopaminergic pathways (VTA, SNc), pain modulation (PAG)","response_latency_ms":{"value":8,"range":[7,9],"measure":"BAEP Wave V (inferior colliculus vicinity)","notes":"BAEP Wave V: 7-9ms. Inferior colliculus response. Superior colliculus visual saccade response: ~40-70ms.","citations":["NCBI Bookshelf NBK597358: BAEP Wave V generator","Walker et al. 2012 (PMC2852267): Superior colliculus to STN short-latency visual input"],"confidence":"HIGH"},"oscillation_bands":["gamma (30-100 Hz)"],"connections":["pons","thalamus","substantia_nigra","stn","superior_colliculus","pag"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The midbrain (mesencephalon) is the smallest brainstem segment but contains critical nuclei for movement, pain, reward, and arousal. The superior colliculus directs rapid eye movements (saccades) and visual orienting reflexes — when something flashes in your peripheral vision, the superior colliculus snaps your gaze toward it before you're consciously aware. The inferior colliculus is a mandatory relay for auditory processing. The periaqueductal gray (PAG) is the brain's pain control center — it can suppress pain signals through descending inhibition and is the reason why soldiers sometimes don't feel wounds during battle.","sub_structures":[{"name":"Superior colliculus","role":"Visual orienting. Saccadic eye movements. Multisensory integration map (visual + auditory + tactile)."},{"name":"Inferior colliculus","role":"Auditory relay. All ascending auditory information passes through. Sound localization integration."},{"name":"Periaqueductal gray (PAG)","role":"Pain suppression center. Descending pain modulation via endogenous opioids. Also: defensive behaviors (fight/flight/freeze)."},{"name":"Red nucleus","role":"Motor control via rubrospinal tract. Receives cerebellar corrections (interposed nuclei). Arm/hand movement (in primates)."},{"name":"Cerebral peduncles","role":"Massive fiber bundle carrying all corticospinal and corticopontine tracts. Motor highway between cortex and brainstem/spinal cord."}],"processing_pipeline":"VISUAL ORIENTING: Peripheral stimulus → Retina → Superior colliculus (spatial map) → Saccade command → Brainstem saccade generator → Eye muscles → Gaze shift (~200ms total). PAIN MODULATION: Threatening stimulus → PAG activation → Releases endorphins → Inhibits dorsal horn pain neurons (spinal cord) → Pain signals blocked from reaching brain. DBS of PAG mimics this.","signal_type":"Multimodal integration — visual orienting, auditory relay, pain modulation, motor relay","bci_relevance_detail":"PAG-DBS is used for chronic pain management. Superior colliculus signals could inform gaze-tracking BCIs. The midbrain contains the SNc and VTA (covered separately) which are the dopaminergic nuclei relevant to movement and reward BCIs.","clinical_notes":"Midbrain stroke (Weber syndrome): ipsilateral oculomotor nerve palsy (drooping eyelid, dilated pupil) + contralateral hemiparesis. Parinaud syndrome (dorsal midbrain lesion): inability to look upward, convergence problems. PAG lesions → increased pain sensitivity (hyperalgesia)."},{"id":"reticular_formation","name":"Reticular Formation","abbreviation":"RF","qif_band":"N2","parent_structure":"brainstem","depth_class":"infratentorial","hemisphere":"midline","function":"Arousal, consciousness, sleep-wake transitions, pain modulation, postural tone","response_latency_ms":{"value":10,"range":[5,50],"measure":"Stimulus to arousal response","notes":"Reticular activating system (RAS) response to salient stimuli: ~10-50ms. Startle reflex (reticulospinal): ~14ms. Muscle tone modulation: 10-20ms.","citations":["Moruzzi & Magoun 1949 (EEG Clin Neurophysiol): Reticular activating system (classic)","Brown et al. 1991 (Exp Brain Res): Reticulospinal reflex latency"],"confidence":"MEDIUM"},"oscillation_bands":["variable (state-dependent)"],"connections":["thalamus","hypothalamus","all_brainstem_nuclei","spinal_cord"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The reticular formation is a diffuse network of neurons extending through the entire brainstem, functioning as the brain's arousal and consciousness system. The ascending reticular activating system (ARAS) keeps the cortex awake and alert — without it, you fall into coma. It works by sending widespread excitatory projections to the thalamus and cortex using multiple neurotransmitters (acetylcholine, norepinephrine, serotonin, dopamine, histamine). During sleep, ARAS activity decreases and the thalamus switches to burst mode, blocking sensory input from reaching the cortex. The reticular formation also coordinates complex motor patterns like walking, chewing, and breathing.","sub_structures":[{"name":"Ascending reticular activating system (ARAS)","role":"Consciousness and arousal. Projects to thalamus and cortex. Damage → coma."},{"name":"Pedunculopontine nucleus (PPN)","role":"Cholinergic arousal. REM sleep generation. Locomotion initiation — 'mesencephalic locomotor region.'"},{"name":"Gigantocellular reticular nucleus","role":"Motor pattern generation. Coordinates postural adjustments during locomotion. Contains reticulospinal tract neurons."}],"processing_pipeline":"AROUSAL: Sensory input (pain, sound, light) → Reticular formation → ARAS → Thalamus (switches tonic mode, opens sensory gate) + Cortex (widespread depolarization) → Wakefulness. SLEEP: Reduced sensory input + VLPO (hypothalamus) inhibits ARAS → Thalamus enters burst mode → Cortex disconnected from sensory input → Sleep. COMA: ARAS destroyed bilaterally → Cortex receives no activation signal → Unconscious despite intact cortex.","signal_type":"Arousal modulation — multi-neurotransmitter ascending activation, consciousness gating","bci_relevance_detail":"DBS of the reticular formation/ARAS has been explored for disorders of consciousness (minimally conscious state, vegetative state). Central thalamic DBS (which targets ARAS projections) has shown some success in promoting arousal. Understanding ARAS is critical for BCIs in non-responsive patients.","clinical_notes":"Bilateral ARAS damage → coma (cortex intact but not activated). Brainstem death = irreversible ARAS destruction = legal death in most jurisdictions. Narcolepsy involves dysregulation of reticular arousal systems. General anesthesia works partly by suppressing ARAS activity."},{"id":"cervical_cord","name":"Cervical Spinal Cord","abbreviation":"C1-C8","qif_band":"N1","parent_structure":"spinal_cord","depth_class":"spinal","hemisphere":"bilateral","function":"Upper limb motor innervation, diaphragm (C3-C5), neck muscles, upper limb sensation","response_latency_ms":{"value":6,"range":[5,10],"measure":"Corticospinal tract to cervical motor neuron","notes":"Central motor conduction time to upper limbs: ~6-8ms (TMS). Peripheral conduction adds ~10-15ms.","citations":["Rossini et al. 2015: TMS guidelines"],"confidence":"HIGH"},"oscillation_bands":null,"connections":["medulla","brainstem","peripheral_nerves_upper"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The cervical spinal cord (C1-C8) carries motor commands to the arms, hands, and diaphragm, and returns sensory information from these areas to the brain. It's organized with white matter (myelinated axons, long-distance pathways) on the outside and gray matter (neuronal cell bodies, local processing) in a butterfly-shaped center. Motor neurons in the ventral horn send signals to muscles. Sensory neurons enter through the dorsal horn. The cervical enlargement (C5-T1) contains extra motor neurons for the complex hand movements that distinguish humans from other primates. The phrenic nerve (C3-C5) controls the diaphragm — 'C3, 4, 5 keeps the diaphragm alive' is a medical mnemonic.","sub_structures":[{"name":"Ventral horn","role":"Motor neuron cell bodies. Alpha motor neurons → skeletal muscle. Gamma motor neurons → muscle spindles."},{"name":"Dorsal horn","role":"Sensory processing. Receives pain, temperature, touch input. Gate control theory of pain operates here."},{"name":"Lateral corticospinal tract","role":"Crossed motor pathway from M1. Voluntary fine motor control, especially hands and fingers."},{"name":"Dorsal columns","role":"Ascending sensory pathway. Fine touch, proprioception, vibration. Fasciculus gracilis (legs) and cuneatus (arms)."},{"name":"Cervical enlargement (C5-T1)","role":"Expanded gray matter for arm/hand motor control. Contains motor neuron pools for biceps, triceps, hand muscles."}],"processing_pipeline":"MOTOR: M1 → Corticospinal tract → Pyramidal decussation (medulla) → Lateral corticospinal tract → Ventral horn motor neurons (C5-T1) → Peripheral nerve → Neuromuscular junction → Arm/hand muscle contraction. SENSORY: Skin/joint receptors → Peripheral nerve → Dorsal root ganglion → Dorsal horn (pain/temp via spinothalamic) or Dorsal columns (touch/proprioception) → Brainstem → Thalamus → S1. REFLEX: Tendon stretch → Muscle spindle → Dorsal root → Direct synapse on ventral horn motor neuron → Muscle contraction (monosynaptic reflex arc, ~30ms).","signal_type":"Motor/sensory relay — corticospinal commands down, somatosensory signals up, local reflex arcs","bci_relevance_detail":"Spinal cord injury at C4-C5 is the most common BCI indication — patients retain brain activity but lose motor output. Epidural spinal stimulation below the injury can reactivate spinal circuits for standing/stepping. BCI bridges: decode motor intent from M1 → stimulate spinal cord below lesion → restore movement. Onward Medical and others targeting this.","clinical_notes":"C4-C5 injury → quadriplegia (most common SCI level). Above C3 → ventilator-dependent (phrenic nerve destroyed). Central cord syndrome: hyperextension injury → hand weakness > leg weakness (central gray matter damage). Brown-Séquard: hemisection → ipsilateral paralysis + contralateral pain/temp loss."},{"id":"thoracic_cord","name":"Thoracic Spinal Cord","abbreviation":"T1-T12","qif_band":"N1","parent_structure":"spinal_cord","depth_class":"spinal","hemisphere":"bilateral","function":"Trunk muscles, sympathetic outflow, intercostal muscles for respiration","response_latency_ms":{"value":10,"range":[8,15],"measure":"Corticospinal conduction to thoracic segments","notes":"Longer conduction distance than cervical. Sympathetic preganglionic neurons here.","citations":[],"confidence":"LOW"},"oscillation_bands":null,"connections":["cervical_cord","lumbar_cord","sympathetic_chain"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The thoracic spinal cord (T1-T12) controls trunk muscles (intercostals for breathing, abdominals for posture) and contains the intermediolateral cell column (IML) — the origin of all sympathetic nervous system output. Every sympathetic 'fight-or-flight' response (increased heart rate, dilated pupils, sweating, adrenaline release) starts with preganglionic sympathetic neurons in the thoracic IML. Sensory input from the trunk (chest, abdomen, back) enters through thoracic dorsal roots.","sub_structures":[{"name":"Intermediolateral column (IML)","role":"Sympathetic preganglionic neurons. ENTIRE sympathetic nervous system originates here (T1-L2). Fight-or-flight."},{"name":"Clarke's column (nucleus dorsalis)","role":"Proprioceptive relay. Sends lower limb proprioception to cerebellum via dorsal spinocerebellar tract."},{"name":"Lateral horn","role":"Contains IML. Only present at thoracic and upper lumbar levels. Autonomic motor output."}],"processing_pipeline":"SYMPATHETIC: Hypothalamus/brainstem → Descending autonomic pathways → IML (T1-L2) → Preganglionic sympathetic axons → Sympathetic chain ganglia → Postganglionic axons → Target organs (heart, lungs, blood vessels, sweat glands, pupils, adrenal medulla). MOTOR: Corticospinal tract → Thoracic ventral horn → Intercostal nerves → Respiratory muscles (T1-T12) and abdominal muscles.","signal_type":"Autonomic/motor — sympathetic fight-or-flight origin, trunk motor control, respiratory muscles","bci_relevance_detail":"Thoracic SCI eliminates sympathetic control below the lesion → autonomic dysreflexia (dangerous blood pressure spikes from uncontrolled sympathetic responses below injury). Epidural stimulation at thoracic levels is being explored for autonomic function restoration (blood pressure, bladder, sexual function).","clinical_notes":"T6 SCI → paraplegia with intact arms. Above T6: risk of autonomic dysreflexia (uncontrolled sympathetic response to below-level stimuli → hypertensive crisis). Thoracic disc herniation is rare (<1% of disc herniations) but can cause myelopathy. Horner syndrome: T1 sympathetic disruption → ipsilateral ptosis, miosis, anhidrosis."},{"id":"lumbar_cord","name":"Lumbar Spinal Cord","abbreviation":"L1-L5","qif_band":"N1","parent_structure":"spinal_cord","depth_class":"spinal","hemisphere":"bilateral","function":"Lower limb motor innervation, patellar reflex, lower limb sensation","response_latency_ms":{"value":14,"range":[12,20],"measure":"Corticospinal conduction to lumbar motor neurons","notes":"Central motor conduction time to lower limbs: ~14-16ms (TMS). Patellar reflex arc: ~15-30ms total.","citations":["Rossini et al. 2015: TMS guidelines"],"confidence":"MEDIUM"},"oscillation_bands":null,"connections":["thoracic_cord","sacral_cord","peripheral_nerves_lower"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The lumbar spinal cord (L1-L5) contains the lumbar enlargement — expanded gray matter housing motor neurons for the lower extremities (hip, knee, ankle, foot). The lumbar cord contains central pattern generators (CPGs) — neural circuits that can generate rhythmic walking movements even without input from the brain. This is why epidural stimulation below a spinal cord injury can restore stepping movements — the CPGs are intact, they just need activation. The lumbar cord also contains sensory processing circuits for the legs and handles the knee-jerk (patellar) reflex at L3-L4.","sub_structures":[{"name":"Lumbar enlargement (L2-S1)","role":"Motor neuron pools for quadriceps, hamstrings, tibialis anterior, gastrocnemius. Leg movement."},{"name":"Central pattern generators (CPGs)","role":"Rhythmic locomotion circuits. Generate alternating flexion-extension patterns for walking WITHOUT brain input."},{"name":"Dorsal horn (lumbar)","role":"Sensory processing for legs. Pain gate control. Referred pain processing."}],"processing_pipeline":"VOLUNTARY: Cortex → Corticospinal tract → Lumbar ventral horn motor neurons → Femoral nerve (L2-L4, knee extension), Sciatic nerve (L4-S3, hip extension/knee flexion/ankle) → Leg muscles. REFLEX: Patellar tendon tap → Muscle spindle stretch → L3-L4 dorsal root → Monosynaptic reflex → Quadriceps motor neuron → Knee jerk (~25ms). CPG: Tonic descending input (or epidural stimulation) → Lumbar CPG activation → Alternating L/R flexor-extensor patterns → Rhythmic stepping.","signal_type":"Motor/CPG — voluntary leg control, central pattern generation for locomotion, lower limb reflexes","bci_relevance_detail":"Lumbar epidural stimulation is a leading BCI approach for restoring walking after SCI. STIMO (Courtine lab, EPFL) demonstrated that targeted epidural stimulation of lumbar CPGs enables SCI patients to walk with assistance. Combined with brain-spine interfaces (BSI), decoded motor cortex signals wirelessly trigger lumbar stimulation in real-time.","clinical_notes":"Lumbar SCI → paraplegia with preserved arms and trunk. Cauda equina syndrome (below L1-L2): LMN injury → flaccid paralysis, bladder/bowel dysfunction, saddle anesthesia — surgical emergency. Lumbar stenosis: compression of lumbar nerves → neurogenic claudication (leg pain/weakness with walking)."},{"id":"sacral_cord","name":"Sacral Spinal Cord","abbreviation":"S1-S5","qif_band":"N1","parent_structure":"spinal_cord","depth_class":"spinal","hemisphere":"bilateral","function":"Bladder, bowel, sexual function, perineal sensation, parasympathetic outflow","response_latency_ms":{"value":18,"range":[15,25],"measure":"Corticospinal conduction to sacral segments","notes":"Longest corticospinal conduction. Pudendal nerve latency: ~35-40ms total (central + peripheral).","citations":[],"confidence":"LOW"},"oscillation_bands":null,"connections":["lumbar_cord","cauda_equina","parasympathetic_outflow"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The sacral spinal cord (S1-S5) contains the parasympathetic nervous system's lower division — controlling bladder, bowel, and sexual function. The sacral micturition center (S2-S4, Onuf's nucleus) coordinates urination through a complex reflex: when the bladder fills, stretch receptors signal the sacral cord, which (with brainstem approval via the pontine micturition center) triggers detrusor muscle contraction and sphincter relaxation. Sacral segments also control the muscles of the pelvic floor, lower limb muscles (foot intrinsics), and carry sensory information from the perineum.","sub_structures":[{"name":"Onuf's nucleus (S2-S4)","role":"Motor neurons for external urethral and anal sphincters. Voluntary continence control."},{"name":"Sacral parasympathetic nucleus (S2-S4)","role":"Parasympathetic preganglionic neurons. Bladder contraction, bowel motility, sexual arousal (erection)."},{"name":"Pudendal nerve origin (S2-S4)","role":"Mixed nerve. Sensory from perineum, motor to pelvic floor muscles, parasympathetic to genitalia."}],"processing_pipeline":"MICTURITION: Bladder stretch receptors → Pelvic nerve → Sacral cord (S2-S4) → Ascending to pontine micturition center (permission signal) → Descending back to sacral parasympathetic nucleus → Detrusor contraction + Internal sphincter relaxation → Onuf's nucleus relaxes external sphincter → Urination. SEXUAL: Psychogenic (cortex → thoracolumbar sympathetic) + Reflexogenic (genital stimulation → sacral parasympathetic S2-S4) → Erection/lubrication.","signal_type":"Parasympathetic/somatic — bladder/bowel/sexual autonomic control, pelvic floor motor","bci_relevance_detail":"Sacral nerve stimulation (InterStim, Medtronic) is FDA-approved for overactive bladder and fecal incontinence. Sacral neuromodulation is a major quality-of-life target for SCI patients — surveys consistently show bladder/bowel/sexual function recovery is ranked higher than walking recovery by SCI patients.","clinical_notes":"SCI above sacral cord → neurogenic bladder (loss of voluntary control, reflex voiding). Conus medullaris syndrome (S1-S5 damage): areflexic bladder/bowel, saddle anesthesia, erectile dysfunction. Cauda equina syndrome: similar presentation but involves nerve roots, not cord — potentially reversible if decompressed within 48 hours."},{"id":"cauda_equina","name":"Cauda Equina","abbreviation":"CE","qif_band":"N1","parent_structure":"spinal_cord","depth_class":"spinal","hemisphere":"bilateral","function":"Bundle of spinal nerve roots below L1-L2. Lower limb motor/sensory, bladder, bowel.","response_latency_ms":{"value":null,"range":null,"measure":null,"notes":"Peripheral nerve bundle, not a CNS structure. Conduction velocity depends on individual nerve roots (~50-60 m/s).","citations":[],"confidence":"LOW"},"oscillation_bands":null,"connections":["lumbar_cord","sacral_cord","peripheral_nerves"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The cauda equina ('horse's tail') is not a spinal cord structure — it's a bundle of nerve roots (L2-S5) that dangle below where the spinal cord ends (conus medullaris, at ~L1-L2 vertebral level). These are peripheral nerves, not central nervous system, which means they can potentially regenerate (unlike spinal cord). The cauda equina carries motor commands to the legs, feet, bladder, bowel, and sexual organs, and returns sensory information from these areas. Because the nerve roots float freely in cerebrospinal fluid within the spinal canal, they're vulnerable to compression from disc herniation, tumors, or spinal stenosis.","sub_structures":[{"name":"Lumbar nerve roots (L2-L5)","role":"Motor: hip flexion, knee extension, ankle dorsiflexion, foot eversion. Sensory: anterior/lateral thigh, shin, dorsal foot."},{"name":"Sacral nerve roots (S1-S5)","role":"Motor: ankle plantarflexion, toe flexion, pelvic floor. Sensory: posterior thigh, sole of foot, perineum."},{"name":"Filum terminale","role":"Thin filament anchoring conus medullaris to coccyx. Contains no neural tissue. Tethered cord if abnormally tight."}],"processing_pipeline":"MOTOR: Spinal cord (conus medullaris) → Ventral roots → Cauda equina nerve roots → Exit at respective vertebral foramina → Peripheral nerves (femoral, sciatic, pudendal) → Muscles. SENSORY: Receptors (legs, perineum, bladder) → Peripheral nerves → Dorsal roots in cauda equina → Enter spinal cord (or ascend to higher cord levels) → Brain. DAMAGE PATTERN: Unlike cord injury (UMN signs), cauda equina injury → LMN signs (flaccid weakness, areflexia) because these are peripheral nerve roots.","signal_type":"Peripheral motor/sensory — lower limb and pelvic nerve root bundle","bci_relevance_detail":"Cauda equina nerve roots are peripheral (potential regeneration), making them targets for nerve root stimulation and peripheral nerve interfaces. Less relevant for cortical BCIs but important for understanding the full neural pathway from brain to muscle.","clinical_notes":"Cauda equina syndrome: SURGICAL EMERGENCY. Compression (usually massive disc herniation) → bilateral leg weakness, saddle anesthesia, bladder retention, bowel incontinence. Must decompress within 48 hours or risk permanent deficit. Key differentiator from conus medullaris: cauda equina = LMN signs (flaccid, areflexic), conus = UMN signs (spastic, hyperreflexic)."},{"id":"cea","name":"Central Amygdala","abbreviation":"CeA","qif_band":"N5","parent_structure":"amygdala","depth_class":"subcortical","hemisphere":"bilateral","function":"Autonomic fear output, conditioned fear responses, pain modulation. Subcortical architecture (unlike BLA).","response_latency_ms":{"value":100,"range":[80,200],"measure":"BLA input to CeA autonomic output","notes":"CeA receives processed input from BLA. Output drives autonomic responses (heart rate, freezing) with ~100-200ms latency from stimulus.","citations":["LeDoux 2000 (Annu Rev Neurosci): Emotion circuits"],"confidence":"LOW"},"oscillation_bands":["theta (4-8 Hz)"],"connections":["bla","hypothalamus","brainstem_autonomic","pag"],"allen_atlas_id":null,"brodmann_areas":null,"how_it_works":"The central amygdala (CeA) is the OUTPUT station of the amygdala — while the BLA evaluates whether something is threatening, the CeA executes the fear response. It sends commands to brainstem nuclei that produce every component of the fear reaction: hypothalamus (stress hormones — cortisol, adrenaline), periaqueductal gray (freezing behavior), lateral hypothalamus (sympathetic activation — elevated heart rate, blood pressure), parabrachial nucleus (breathing changes), and nucleus basalis (cortical arousal). The CeA is also critical for learned fear — after Pavlovian fear conditioning, the CeA autonomously drives defensive behaviors without requiring cortical involvement.","sub_structures":[{"name":"CeL (lateral division)","role":"Gate control. Contains 'fear-on' and 'fear-off' neurons that regulate CeM output. Extinction learning modifies CeL activity."},{"name":"CeM (medial division)","role":"Primary output. Projects to brainstem fear effectors. Drives all autonomic and behavioral fear responses."},{"name":"Intercalated cells (ITCs)","role":"GABAergic gate between BLA and CeA. Extinction training strengthens ITC inhibition of CeM → reduced fear expression."}],"processing_pipeline":"BLA (threat detected) → CeA (CeL gate → CeM output) → MULTIPLE PARALLEL OUTPUTS: Hypothalamus PVN (HPA axis → cortisol), Lateral hypothalamus (sympathetic activation → heart rate, blood pressure), PAG (freezing/fight/flight behavior), Parabrachial nucleus (respiratory rate change), Nucleus basalis (acetylcholine → cortical arousal/vigilance), Dorsal vagal complex (gastrointestinal response — 'gut feeling'). EXTINCTION: PFC (infralimbic) → ITCs → Inhibit CeM → Reduced fear expression.","signal_type":"Fear output — autonomic, endocrine, and behavioral fear response execution","bci_relevance_detail":"CeA is not a typical BCI target due to its deep location and functional criticality. However, understanding CeA output pathways is important for BCI safety — inadvertent stimulation near the amygdala could trigger full autonomic fear responses. Relevant for affective state monitoring in emotional BCIs.","clinical_notes":"CeA hyperactivation → panic attacks (full autonomic fear cascade without actual threat). PTSD: CeA fails to extinguish — fear responses persist despite safety. Anxiety disorders: lowered CeA activation threshold. CeA lesions in animals → fearlessness, inability to learn danger associations."}],"device_mappings":[{"device_id":"neuralink-n1","target_regions":["m1"],"qif_bands":["N7"],"i0_depth":"I0-cortical","interface_type":"read","notes":"1024-channel cortical implant in motor cortex. Read-only (recording spikes + LFP)."},{"device_id":"neuralink-n2","target_regions":["m1","v1"],"qif_bands":["N7"],"i0_depth":"I0-cortical","interface_type":"bidirectional","notes":"Next-gen: motor + visual cortex targets. Stimulation planned for vision restoration."},{"device_id":"blackrock-utah-array","target_regions":["m1","s1_cortex"],"qif_bands":["N7"],"i0_depth":"I0-cortical","interface_type":"bidirectional","notes":"96-128 channels, penetrating electrodes. SIROF variant enables stimulation. Gold standard for cortical recording."},{"device_id":"braingate","target_regions":["m1"],"qif_bands":["N7"],"i0_depth":"I0-cortical","interface_type":"read","notes":"Uses Utah Arrays. Wireless variant: 48 Mbps, >36h battery. First home-use wireless intracortical BCI."},{"device_id":"neuropace-rns","target_regions":["hippocampus","bla","cerebellum_cortex"],"qif_bands":["N6","N3"],"i0_depth":"I0-subcortical","interface_type":"bidirectional","notes":"Responsive neurostimulation at seizure foci. Depth electrodes reach hippocampus, amygdala. Surface strips for cortical foci."},{"device_id":"medtronic-percept-pc","target_regions":["stn","gpi","vim"],"qif_bands":["N5","N4"],"i0_depth":"I0-subcortical","interface_type":"bidirectional","notes":"DBS + BrainSense LFP recording. Primary targets: STN (PD), GPi (dystonia), VIM (tremor)."},{"device_id":"medtronic-percept-rc","target_regions":["stn","gpi","vim"],"qif_bands":["N5","N4"],"i0_depth":"I0-subcortical","interface_type":"bidirectional","notes":"Rechargeable variant. Same brain targets as Percept PC. >15 year service life."},{"device_id":"synchron-stentrode","target_regions":["m1"],"qif_bands":["N7"],"i0_depth":"I0-cortical","interface_type":"read","notes":"Endovascular (superior sagittal sinus). Adjacent to motor cortex but inside blood vessel. No direct tissue contact."},{"device_id":"cochlear-nucleus-profile-plus","target_regions":["a1"],"qif_bands":["N7"],"i0_depth":"I0-spinal/peripheral","interface_type":"write","notes":"Cochlear implant stimulates auditory nerve (CN VIII). Signal reaches A1 via brainstem auditory pathway. 22 electrodes."},{"device_id":"medel-cochlear","target_regions":["a1"],"qif_bands":["N7"],"i0_depth":"I0-spinal/peripheral","interface_type":"write","notes":"Cochlear implant. 12 channels, longest electrode arrays (up to 34mm) for complete cochlear coverage."},{"device_id":"boston-scientific-vercise","target_regions":["stn","gpi","vim"],"qif_bands":["N5","N4"],"i0_depth":"I0-subcortical","interface_type":"write","notes":"DBS stimulation only (no sensing). 16-contact directional leads (Cartesia). 25-year rechargeable battery."},{"device_id":"abbott-infinity-dbs","target_regions":["stn","gpi","vim"],"qif_bands":["N5","N4"],"i0_depth":"I0-subcortical","interface_type":"write","notes":"DBS with directional stimulation. Smallest non-rechargeable DBS device at launch. 8-contact segmented leads."},{"device_id":"emotiv-epoc-x","target_regions":["pfc","ppc","v1","a1","m1"],"qif_bands":["N7"],"i0_depth":"I0-noninvasive","interface_type":"read","notes":"14-channel consumer EEG. Scalp recording. Volume conduction limits spatial resolution to ~cm scale. Saline felt sensors."},{"device_id":"emotiv-insight","target_regions":["pfc","ppc"],"qif_bands":["N7"],"i0_depth":"I0-noninvasive","interface_type":"read","notes":"5-channel consumer EEG. Minimal coverage: frontal + temporal + parietal."},{"device_id":"openbci-cyton","target_regions":["pfc","m1","ppc","v1","a1"],"qif_bands":["N7"],"i0_depth":"I0-noninvasive","interface_type":"read","notes":"8-channel (expandable to 16) open-source EEG. ADS1299 24-bit ADC. Research-grade for non-invasive BCI."},{"device_id":"openbci-ganglion","target_regions":["pfc","m1"],"qif_bands":["N7"],"i0_depth":"I0-noninvasive","interface_type":"read","notes":"4-channel entry-level open-source EEG. BLE. 18-bit effective resolution."},{"device_id":"muse-2","target_regions":["pfc"],"qif_bands":["N7"],"i0_depth":"I0-noninvasive","interface_type":"read","notes":"4-channel consumer EEG headband. Frontal + temporal positions. Dry electrodes. Primary use: meditation."},{"device_id":"muse-s","target_regions":["pfc"],"qif_bands":["N7"],"i0_depth":"I0-noninvasive","interface_type":"read","notes":"4 EEG + 2 auxiliary channels. Sleep tracking focus. Same electrode positions as Muse 2."},{"device_id":"corticom","target_regions":["broca","m1"],"qif_bands":["N7"],"i0_depth":"I0-cortical","interface_type":"read","notes":"128-channel ECoG (subdural). Speech + hand motor areas. Temporary (6-month) implantation. Hopkins consortium."},{"device_id":"paradromics-connexus","target_regions":["m1","broca"],"qif_bands":["N7"],"i0_depth":"I0-cortical","interface_type":"read","notes":"421-1681 channels. Intracortical microwires (1.5mm depth). Near-infrared optical data link (not RF)."},{"device_id":"precision-layer7","target_regions":["m1","s1_cortex"],"qif_bands":["N7"],"i0_depth":"I0-cortical","interface_type":"bidirectional","notes":"1024-channel thin-film cortical array (5um thick). Minimally invasive: inserted through <1mm slit. 510(k) cleared for intraoperative use."},{"device_id":"gtec-unicorn","target_regions":["pfc","m1","ppc"],"qif_bands":["N7"],"i0_depth":"I0-noninvasive","interface_type":"read","notes":"8-channel research EEG. Hybrid dry/gel electrodes. 24-bit ADC. Bluetooth."},{"device_id":"kernel-flow","target_regions":["pfc","m1","ppc","v1","a1"],"qif_bands":["N7"],"i0_depth":"I0-noninvasive","interface_type":"read","notes":"2000+ optical channels (TD-fNIRS). Hemodynamic imaging (not electrical). 52 modules. Whole-head coverage."},{"device_id":"darpa-n3","target_regions":["m1","pfc"],"qif_bands":["N7"],"i0_depth":"I0-noninvasive","interface_type":"bidirectional","notes":"Research program (5 teams). Target: 16 channels in 16mm3 within 50ms. Approaches: acousto-optical, magnetoelectric nanotransducers, OPMs."}],"neural_latency":[],"physics_constraints":[]},"physics":{"constraints":[{"id":1,"name":"Thermal Power Ceiling","equation":"P_total(n_ch, node_nm) <= P_thermal(R, n_chips, geometry, perfusion)","explanation":"Total power dissipation must stay below the thermal limit set by brain region, chip count, implant geometry, and local blood perfusion. Exceeding this causes tissue damage.","category":"thermo-power"},{"id":2,"name":"Wireless Carrier Frequency","equation":"f_carrier <= f_max(tissue_attenuation, d)","explanation":"The wireless carrier frequency is limited by tissue attenuation at the implant depth. Higher frequencies lose more energy traveling through brain tissue.","category":"em-wireless"},{"id":3,"name":"On-Chip Clock Frequency","equation":"f_clock <= f_max_clk(P_budget, C_load, V_dd)","explanation":"The on-chip clock speed is bounded by dynamic power dissipation (P ~ C * V^2 * f). Faster clocks burn more power, which feeds back into the thermal ceiling.","category":"thermo-power"},{"id":4,"name":"Moore's Law Scaling","equation":"n_ch(t) = n_ch(0) * 2^(t / T_double)","explanation":"BCI channel count doubles approximately every 7.4 years (Stevenson & Kording 2011). This governs when future attack techniques become feasible as hardware scales.","category":"scaling-geometry"},{"id":5,"name":"Shannon Electrode Safety","equation":"k = log(D) + log(Q) < 1.75","explanation":"The Shannon safety limit constrains stimulation charge density (D) and charge per phase (Q). Exceeding k = 1.75 risks tissue damage from electrolysis and reactive oxygen species.","category":"safety-bio"},{"id":6,"name":"Signal Detectability (SNR)","equation":"V_spike / V_noise_rms >> 1, where V_noise = sqrt(4kT * Re(Z) * df)","explanation":"Neural spikes must exceed the Johnson-Nyquist thermal noise floor. At body temperature (310K) with 1 MOhm impedance and 10 kHz bandwidth, noise is ~13.1 uV rms.","category":"signal-detection"},{"id":7,"name":"QIF Coherence Threshold","equation":"Cs(t) >= Cs_min(F)","explanation":"The QIF signal coherence metric must stay above a minimum threshold for each brain function F. When Cs drops below Cs_min, the signal is either degraded, corrupted, or under attack.","category":"signal-detection"},{"id":8,"name":"Thermal Ceiling (Coupled)","equation":"DeltaT_total = f(P_total, geometry, perfusion) <= 1.0C","explanation":"Total temperature rise must stay below 1.0C (AAMI conservative guideline). This is coupled to constraint 1 via the Pennes bioheat equation. They are not independent.","category":"thermo-power"},{"id":9,"name":"Mechanical Mismatch","equation":"E_implant / E_brain < epsilon_safe","explanation":"The ratio of implant stiffness to brain tissue stiffness must remain below a safe threshold. Silicon is ~6 orders of magnitude stiffer than brain tissue, causing micromotion damage.","category":"safety-bio"},{"id":10,"name":"Impedance Timeline","equation":"Z_electrode(t) <= Z_max(signal_type)","explanation":"Electrode impedance rises over time due to gliosis (scar tissue formation). It must stay below the maximum for the target signal type, or recording quality degrades irreversibly.","category":"scaling-geometry"},{"id":11,"name":"Geometric Fit","equation":"V_implant(n_ch, packaging) <= V_max(R)","explanation":"The physical volume of the implant (determined by channel count and packaging) must fit within the target brain region. This limits maximum channel density per implant.","category":"scaling-geometry"},{"id":12,"name":"Information-Theoretic Minimum","equation":"I_Shannon = B * log2(1 + SNR) >= I_min(F)","explanation":"The Shannon channel capacity must meet the minimum information rate required for the target function. This is a hard floor: no encoding scheme can beat it.","category":"em-wireless"},{"id":13,"name":"Wireless Telemetry Bandwidth","equation":"BW_telemetry >= n_ch * f_sample * bit_depth","explanation":"Total wireless data rate must accommodate all channels at their sampling rate and bit depth. This constrains how many channels can transmit simultaneously over the wireless link.","category":"em-wireless"}],"categories":[{"id":"thermo-power","label":"Thermodynamics & Power","color":"var(--color-accent-primary)"},{"id":"em-wireless","label":"Electromagnetic & Wireless","color":"var(--color-accent-secondary)"},{"id":"scaling-geometry","label":"Scaling & Geometry","color":"var(--color-accent-tertiary)"},{"id":"safety-bio","label":"Safety & Biocompatibility","color":"var(--color-status-warning)"},{"id":"signal-detection","label":"Signal & Detection","color":"var(--color-status-safe)"}],"constants":[{"parameter":"Max safe tissue temp rise","value":"1.0°C","source":"AAMI guideline (conservative)","status":"Corrected attribution"},{"parameter":"Max intracortical power (single 2x2mm chip)","value":"4.8–8.4 mW","source":"Kim et al., Marblestone et al. 2013","status":"Corrected"},{"parameter":"Max intracortical power (distributed/epidural)","value":"15–40 mW","source":"Published BCI thermal analyses","status":"Verified"},{"parameter":"Neural spike bandwidth","value":"300–10,000 Hz","source":"Neurophysiology","status":"Verified"},{"parameter":"Spike amplitude","value":"40–500 µV","source":"Neurophysiology","status":"Verified"},{"parameter":"Spike detection range","value":"50–140 µm","source":"Electrode characterization","status":"Verified"},{"parameter":"Thermal noise floor (kT at 310K)","value":"4.28 × 10⁻²¹ J","source":"Boltzmann constant","status":"Verified"},{"parameter":"Johnson noise (1 MΩ, 10 kHz BW, 310K)","value":"~13.1 µV rms","source":"sqrt(4kT·Re(Z)·df), T=310K","status":"Corrected"},{"parameter":"Shannon safety limit (k)","value":"1.75–1.85","source":"Shannon 1992, AAMI, DBS lit.","status":"Verified"},{"parameter":"Neuronal kill zone","value":"40–150 µm","source":"Implant pathology","status":"Corrected"},{"parameter":"Brain micromotion (cardiac)","value":"1–4 µm","source":"Biomechanics","status":"Corrected"},{"parameter":"Brain micromotion (all sources)","value":"10–30 µm","source":"Cardiac + respiratory + postural","status":"Clarified"},{"parameter":"BCI channel doubling time","value":"~7.4 yr","source":"Stevenson & Kording 2011","status":"Corrected"},{"parameter":"DC leakage tissue damage threshold","value":"0.4 µA","source":"Preclinical studies (PMC6049833)","status":"Added"}],"validation":{"validator":"Gemini 2.5 Pro","phase":9,"constraintsVerified":12,"constraintsTotal":13,"corrections":["Constraint 9 (mechanical mismatch): inverted ratio corrected to E_implant/E_brain < epsilon_safe","Johnson noise temperature corrected from 300K to 310K (body temperature): ~13.1 µV rms"]}},"specs":{"niss":{"version":"1.1","metrics":{"BI":{"name":"Biological Impact","values":{"N":0,"L":3.3,"H":6.7,"C":10},"description":"Severity of tissue damage from neural interface attacks"},"CR":{"name":"Cognitive Reconnaissance","values":{"N":0,"L":3.3,"H":6.7,"C":10},"description":"Degree of unauthorized cognitive read access: thought decoding, neural data inference, intent extraction"},"CD":{"name":"Cognitive/Functional Disruption","values":{"N":0,"L":3.3,"H":6.7,"C":10},"description":"Degree of unauthorized disruption to cognitive processing, sensory perception, motor output, or autonomic regulation"},"CV":{"name":"Consent Violation","values":{"N":0,"P":3.3,"E":6.7,"I":10},"description":"Whether the attack violates neural data consent"},"RV":{"name":"Reversibility","values":{"F":0,"T":3.3,"P":6.7,"I":10},"description":"Whether neural or biological damage can be reversed"},"NP":{"name":"Neuroplasticity","values":{"N":0,"T":3.3,"P":6.7,"S":10},"description":"Whether the attack causes lasting neural pathway changes"}},"formula":"NISS = sum(w_i * M_i) / sum(w_i), default weights: BI=1.0, CR=0.5, CD=0.5, CV=1.0, RV=1.0, NP=1.0","weights":{"default":{"BI":1,"CR":0.5,"CD":0.5,"CV":1,"RV":1,"NP":1}},"context_profiles":{"clinical":{"BI":2,"CR":1,"CD":2,"CV":1,"RV":2,"NP":1},"research":{"BI":1,"CR":2,"CD":1.5,"CV":2,"RV":1,"NP":1.5},"consumer":{"BI":1,"CR":2,"CD":1,"CV":2,"RV":1,"NP":1},"military":{"BI":2,"CR":2,"CD":2,"CV":0.5,"RV":1.5,"NP":1.5}},"pins":{"description":"Potential Impact to Neural Safety","trigger":"BI >= H OR RV == I","type":"boolean"},"severity_scale":{"none":"0.0","low":"0.1-3.9","medium":"4.0-6.9","high":"7.0-8.9","critical":"9.0-10.0"},"rounding":"ceil(score * 10) / 10","vector_format":"NISS:1.1/BI:<v>/CR:<v>/CD:<v>/CV:<v>/RV:<v>/NP:<v>"},"tara":{"version":"1.0","name":"TARA — Therapeutic Applications & Risk Assessment","description":"Mechanism-first Rosetta Stone. Same physical phenomenon, four stakeholder views. Security researchers see attack vectors. Clinicians see therapeutic modalities. Regulators see compliance requirements. Engineers see physical parameters.","dual_use_classifications":{"confirmed":"Published therapeutic and security applications exist for this mechanism","probable":"Strong theoretical basis for dual-use; therapeutic research underway","possible":"Mechanism could have therapeutic applications but no published evidence","silicon_only":"Pure digital/firmware/infrastructure attack with no neural therapeutic analog"},"consent_tiers":{"standard":"Standard informed consent (data collection, non-invasive monitoring)","enhanced":"BCI-specific consent (stimulation, neural recording, neural data processing)","IRB":"Institutional Review Board approval required (research, novel therapeutic applications)","prohibited":"Application prohibited outside controlled research (unacceptable risk)"},"fda_statuses":{"cleared":"FDA 510(k) clearance for predicate device equivalence","approved":"FDA PMA (Premarket Approval) for Class III devices","breakthrough":"FDA Breakthrough Device designation (expedited review)","investigational":"Under IDE (Investigational Device Exemption)","none":"No FDA regulatory pathway established","N/A":"Not applicable (silicon-only or non-device mechanism)"},"evidence_levels":{"meta_analysis":"Systematic reviews and meta-analyses (highest)","RCT":"Randomized controlled trials","cohort":"Observational cohort studies","case_series":"Case series and reports","preclinical":"Animal or in-vitro studies","theoretical":"No empirical evidence yet","N/A":"Not applicable"},"data_classifications":{"PHI":"Protected Health Information (HIPAA/HITECH)","sensitive_neural":"Neural data with enhanced protections (proposed neurorights)","PII":"Personally identifiable information (GDPR Art. 9 special category)","restricted":"Restricted access (need-to-know basis)","internal":"Internal operational data","public":"Publicly available information"}},"dsm5":{"version":"1.0","name":"DSM-5-TR Diagnostic Mapping via Neural Impact Chain","description":"Maps BCI techniques to psychiatric diagnoses through the hourglass band model. NISS metrics predict diagnostic clusters. First known formal BCI threat-to-psychiatric-diagnosis taxonomy.","methodology":"Band → Structure → Function → NISS (quantitative) + DSM (qualitative)","positioning":"RDoC-aligned, BCI-specific, with quantitative bridge (NISS) to traditional nosology (DSM-5-TR)","band_profiles":{"N7":{"structures":["PFC","M1","V1","Broca","Wernicke"],"functions":["executive function","language","movement","perception"],"primary_codes":["F20","F32","F90","F42"],"secondary_codes":["F30","F43","F80","F60","F63","F01","F98.4"]},"N6":{"structures":["hippocampus","amygdala","insula"],"functions":["emotion regulation","memory consolidation","interoception"],"primary_codes":["F32","F41.1","F43.10","F44"],"secondary_codes":["F30","F42","F50","F10","F60","F45","F63","F01"]},"N5":{"structures":["striatum","STN","substantia nigra"],"functions":["motor selection","reward processing","habit formation"],"primary_codes":["F90","F10","F42","F95"],"secondary_codes":["F20","F50","G25.89"]},"N4":{"structures":["thalamus","hypothalamus"],"functions":["sensory gating","consciousness","homeostasis"],"primary_codes":["F20","G47","F44"],"secondary_codes":["F32","F50","F52"]},"N3":{"structures":["cerebellar cortex","deep cerebellar nuclei"],"functions":["motor coordination","timing","cognitive integration"],"primary_codes":["F82","F84"],"secondary_codes":["F20","F90","F30","F32","F41.1","F01"]},"N2":{"structures":["medulla","pons","midbrain"],"functions":["vital functions","arousal","neurotransmitter production"],"primary_codes":["G47"],"secondary_codes":["F32","F41.0","F10","F01"]},"N1":{"structures":["spinal cord"],"functions":["reflexes","peripheral relay","pain processing"],"primary_codes":["F45","F44.4"],"secondary_codes":["F82"]},"I0":{"structures":["electrode-tissue boundary"],"functions":["measurement","signal transduction"],"primary_codes":["F43.2"],"secondary_codes":[]},"S1":{"structures":["ASIC/analog front-end"],"functions":["hardware processing"],"primary_codes":[],"secondary_codes":[]},"S2":{"structures":["firmware/DSP"],"functions":["signal processing"],"primary_codes":[],"secondary_codes":[]},"S3":{"structures":["network/cloud"],"functions":["data pipeline"],"primary_codes":[],"secondary_codes":[]}},"niss_dsm_bridge":{"BI":{"risk_domain":"Structural/Tissue damage","primary_clusters":["motor_neurocognitive"],"dsm_chapters":["Motor (F82/F95)","Neurocognitive (F01-G31)"]},"CR":{"risk_domain":"Cognitive read access (thought decoding, neural data inference)","primary_clusters":["cognitive_psychotic"],"dsm_chapters":["Neurodevelopmental (F70-F98)","Schizophrenia Spectrum (F20-F29)"]},"CD":{"risk_domain":"Cognitive write access (perception manipulation, identity modification)","primary_clusters":["cognitive_psychotic"],"dsm_chapters":["Neurodevelopmental (F70-F98)","Schizophrenia Spectrum (F20-F29)"]},"CV":{"risk_domain":"Consent/autonomy violation","primary_clusters":["mood_trauma"],"dsm_chapters":["Depressive (F32-F34)","Anxiety (F40-F41)","Trauma/PTSD (F43)","Dissociative (F44)"]},"RV":{"risk_domain":"Chronicity modifier","primary_clusters":["persistent_personality"],"dsm_chapters":["Distinguishes acute (F43.2) vs persistent (F34.1) presentations"]},"NP":{"risk_domain":"Lasting neural change","primary_clusters":["persistent_personality"],"dsm_chapters":["Personality (F60-F69)","Neurodegenerative (G30-G31)"]}},"diagnostic_clusters":{"cognitive_psychotic":{"label":"Cognitive/Psychotic","color":"#f59e0b","niss_drivers":["CR","CD","BI"],"dsm_chapters":["Schizophrenia Spectrum (F20-F29)","Neurodevelopmental (F70-F98)","Neurocognitive (F01-G31)"]},"mood_trauma":{"label":"Mood/Trauma","color":"#eab308","niss_drivers":["CV","CR","CD"],"dsm_chapters":["Depressive (F32-F34)","Anxiety (F40-F41)","Trauma/PTSD (F43)","Dissociative (F44)","OCD (F42)"]},"motor_neurocognitive":{"label":"Motor/Neurocognitive","color":"#ef4444","niss_drivers":["BI","NP"],"dsm_chapters":["Motor (F82/F95)","Neurocognitive (F01-G31)","Somatic Symptom (F45)"]},"persistent_personality":{"label":"Persistent/Personality","color":"#a855f7","niss_drivers":["NP","RV"],"dsm_chapters":["Personality (F60-F69)","Neurodegenerative (G30-G31)"]},"non_diagnostic":{"label":"Non-Diagnostic","color":"#94a3b8","niss_drivers":[],"dsm_chapters":[]}}}},"timeline":[{"date":"2026-01-15","type":"release","title":"TARA v1.0: Initial Atlas Published","description":"First public release of the Therapeutic Applications & Risk Assessment atlas. 60 attack techniques. The starting point.","stats_snapshot":{"threat_techniques":60}},{"date":"2026-01-18","type":"milestone","title":"ONI Framework Repository Created","description":"First commit of the original ONI (Open Neural Interface) framework. Predecessor to QIF. 14-layer OSI model. Zero framework, zero scoring, just a question: what does security look like when the endpoint is a human brain?"},{"date":"2026-01-29","type":"release","title":"ONI Demo Video v1.0 Complete","description":"3:56 animated explainer (Remotion + ElevenLabs). L1-L14 visualization, coherence gauge. First public-facing media."},{"date":"2026-02-01","type":"milestone","title":"Qinnovate Website Repository Created","description":"First commit to the qinnovate.com website. Astro 5.x + React 19 + TailwindCSS 4. The public face of the project."},{"date":"2026-02-02","type":"discovery","title":"QIF Born: 14-Layer Model Deprecated, Hourglass Adopted","description":"Single session produced 13 derivation insights. CNF renamed to QIF. 14-layer OSI model (v2.0) deprecated in favor of the 7-band symmetric (3-1-3) hourglass. QIF-TRUTH.md created as canonical source of truth.","stats_snapshot":{"hourglass_bands":7,"derivation_log_entries":13}},{"date":"2026-02-02","type":"validation","title":"First Cross-AI Validation (Gemini 2.5)","description":"Independent AI peer review by Gemini 2.5 of the hourglass model. Validation pipeline established."},{"date":"2026-02-03","type":"discovery","title":"Hamiltonian Root: QI Grounded in Physics Formalism","description":"The QI equation grounded in Hamiltonian formalism. Not a heuristic score but a quantity derived from the system's energy landscape. The unifying mathematical root."},{"date":"2026-02-06","type":"discovery","title":"L=v/f Unification and 11-Band Model","description":"Wavelength equation applied to neural security. Government-restricted spectrum attack mapping added. QIF v4.0: 11 bands implemented (N7-N1, I0, S1-S3). Five coupling mechanisms identified.","stats_snapshot":{"hourglass_bands":11}},{"date":"2026-02-06","type":"milestone","title":"NSP Formalized: 6-Layer Validation Stack","description":"Neural Sensory Protocol designed as a 6-layer validation stack. Physics-based authentication using spectral invariants, microstate compliance, and challenge-response. Biological TLS."},{"date":"2026-02-06","type":"milestone","title":"NSP Goes Post-Quantum","description":"Neural data has a decades-long sensitivity window. Harvest-Now-Decrypt-Later threat model adopted. ML-KEM/ML-DSA integrated. 5 defense layers, Merkle-amortized SPHINCS+ signatures."},{"date":"2026-02-06","type":"discovery","title":"Black Hole Security Principle","description":"Hawking/Susskind/Maldacena information principles applied to BCI security boundaries. Novel theoretical contribution."},{"date":"2026-02-06","type":"release","title":"Unified Neural Security Taxonomy","description":"60 attack techniques across 11 MITRE-compatible tactics spanning 7 domains. First structured BCI threat taxonomy.","stats_snapshot":{"threat_techniques":60}},{"date":"2026-02-07","type":"milestone","title":"NISS Created: BCI-Specific Severity Scoring","description":"NISS (Neural Impact Scoring System) created because CVSS cannot account for biological reversibility, cognitive integrity impact, or neural tissue interaction. A severity metric built for brains, not servers."},{"date":"2026-02-07","type":"validation","title":"First Multi-Model Validation Cycle","description":"QwQ-32B, Grok-3, DeepSeek-R1, WhiteRabbitNeo, and Gemini independently reviewed QIF framework. Consensus reached on hourglass model validity. 5 errors corrected."},{"date":"2026-02-07","type":"milestone","title":"Project Runemate Conceived","description":"Runemate DSL conceived: a domain-specific language for neural rendering. 3-pass Gemini review. NSP unification confirmed."},{"date":"2026-02-08","type":"milestone","title":"NISS v1.0 Finalized","description":"Spectral decomposition bridge connects raw neural signals to per-band QI scoring. Every technique scored for neural impact. Replaces CVSS for BCI context.","stats_snapshot":{"threat_techniques":65}},{"date":"2026-02-09","type":"discovery","title":"Dynamical Systems Security: 6 New Attack Classes","description":"Bifurcation manipulation, basin erosion, strange attractor injection, Lyapunov destabilization, phase space compression, and resonance entrainment. Novel BCI attack vectors from dynamical systems theory.","stats_snapshot":{"threat_techniques":71}},{"date":"2026-02-09","type":"discovery","title":"TARA Reframe: Threat Catalog Becomes Therapeutic Atlas","description":"Attack techniques reframed as dual-use: every neural attack has a therapeutic analog. 35-40 therapeutic counterparts, 10 ambiguous, 18 silicon-only. Registry transformed from threat catalog to risk-benefit atlas."},{"date":"2026-02-09","type":"milestone","title":"Framework Complete (v1): QIF + NSP + NISS + TARA","description":"First complete version of the full stack. 50 derivation log entries. 7 days of formal derivation. 4 framework versions. Ready for independent peer review.","stats_snapshot":{"derivation_log_entries":50,"threat_techniques":71}},{"date":"2026-02-11","type":"release","title":"TARA v1.3: S-Domain Consumer Sensor Expansion","description":"Consumer device attack surface expanded. Synthetic band techniques for off-the-shelf sensors."},{"date":"2026-02-13","type":"release","title":"TARA v1.4: Consumer Side-Channel Techniques","description":"3 new techniques (T0100-T0102). Total: 102 techniques.","stats_snapshot":{"threat_techniques":102}},{"date":"2026-02-13","type":"discovery","title":"TARA-to-DSM-5-TR Diagnostic Mapping","description":"Neural Impact Chain bridges attack techniques to DSM-5-TR diagnostic codes. 5 diagnostic clusters, 68 DSM codes mapped. Every TARA technique now maps to potential psychiatric outcomes.","stats_snapshot":{"dsm5_diagnoses_mapped":68}},{"date":"2026-02-13","type":"discovery","title":"Three Floors of Physics Integration","description":"Landauer limit, Margolus-Levitin bound, and Heisenberg uncertainty integrated into QIF constraint system. P=E/t computational scaling equation derived."},{"date":"2026-02-14","type":"milestone","title":"NSP Core Implementation: Rust ML-KEM + ML-DSA","description":"Post-quantum handshake implemented in Rust. ML-KEM-768 key exchange and ML-DSA-65 signatures working end-to-end. The cryptographic layer is real code, not just a spec."},{"date":"2026-02-14","type":"milestone","title":"Runemate Forge + NSP Integration Verified","description":"End-to-end secure neural pipeline: HTML content compiled to Runemate Staves bytecode, encrypted via NSP post-quantum handshake, decrypted and rendered on target."},{"date":"2026-02-14","type":"release","title":"Zenodo Working Paper v1.0 Published","description":"First public working paper: 28 pages, 6 figures, CC-BY-4.0. DOI: 10.5281/zenodo.18640106. 3 fabricated citations discovered and disclosed."},{"date":"2026-02-16","type":"release","title":"TARA v1.5: Neurorights Mapping","description":"4 neurorights (Mental Privacy, Cognitive Liberty, Mental Integrity, Psychological Continuity) mapped to all 102 techniques. Consent Complexity Index (CCI) introduced.","stats_snapshot":{"neurorights_mapped":4}},{"date":"2026-02-16","type":"release","title":"TARA v1.6: FDORA Regulatory Compliance","description":"FDORA Section 3305 (Patch Act) coverage mapped to all techniques. Regulatory gap analysis complete."},{"date":"2026-02-18","type":"release","title":"BCI Hardware Inventory Published","description":"24 BCI devices catalogued from FDA filings, manufacturer datasheets, and peer-reviewed papers. Full specs with confidence levels.","stats_snapshot":{"bci_devices":24}},{"date":"2026-02-18","type":"release","title":"Brain-BCI Atlas v1.2.0","description":"38 brain structures mapped to BCI devices, QIF bands, neural latency metrics, and physics constraints.","stats_snapshot":{"brain_regions":38}},{"date":"2026-02-18","type":"discovery","title":"BCI Limits Equation Synthesized","description":"13 physics constraints unified into a single system coupling thermodynamics, EM, Moore's Law, Shannon safety, Boltzmann detectability, and QIF coherence. No published paper unifies all 13.","stats_snapshot":{"physics_constraints":13}},{"date":"2026-02-18","type":"milestone","title":"Physics Feasibility Tiering Complete","description":"All 102 TARA techniques classified by physics feasibility tier. 61 feasible now, 11 near-term, 10 mid-term, 2 far-term, 18 no physics gate."},{"date":"2026-02-18","type":"discovery","title":"SSVEP Frequency Hijack (T0103)","description":"Novel attack technique: hijacking steady-state visually evoked potentials via imperceptible screen flicker. Validated by Ming et al. (2023). 103rd technique added.","stats_snapshot":{"threat_techniques":103}},{"date":"2026-02-18","type":"release","title":"Zenodo Working Paper v1.4 Published","description":"NISS score corrections (arithmetic mean), TMS citation fix, dual-use label fix, Sherman & Guillery citation added. DOI: 10.5281/zenodo.18677997"},{"date":"2026-02-18","type":"milestone","title":"NSP Protocol Spec v0.5 Finalized","description":"Protocol core and handshake complete. 5-layer neurosecurity stack, 3 device tiers, power budget modeled at 3.25% of 40 mW (hardware validation pending)."},{"date":"2026-02-21","type":"milestone","title":"Neurowall v0.7: Signal Integrity Monitor","description":"3-layer coherence monitor built from v0.1 to v0.7 in a single day. Multi-band EEG, spectral peak detection, CUSUM change detection, ROC analysis. 7/9 attacks detected at 15s, 9/9 at 30s. 14 attack generators in neurosim toolkit.","stats_snapshot":{"neurowall_version":"v0.8"}},{"date":"2026-02-21","type":"validation","title":"BrainFlow Real Hardware Validation","description":"First test of Neurowall on actual EEG hardware (OpenBCI Cyton via BrainFlow). 5/5 attacks detected, 0% false positive rate. Coherence monitor works on real signals, not just synthetic.","stats_snapshot":{"neurowall_version":"v0.8"}},{"date":"2026-02-21","type":"validation","title":"BCI Limits Equation Cross-Validated (Gemini 2.5 Pro)","description":"12/13 constraints verified correct. 2 corrections applied: mechanical mismatch ratio inverted, Johnson noise temperature corrected to 310K.","stats_snapshot":{"physics_constants_verified":14}},{"date":"2026-02-21","type":"milestone","title":"Validation Dashboard + TARA Badges","description":"Living validation system tracking cross-AI validation sessions, citation checks, and fact verification. Status badges added to every TARA technique page."},{"date":"2026-02-21","type":"milestone","title":"Repository Consolidation: 17 Directories to 8","description":"Major repo restructure. Autodidactive removed, governance merged, archives cleaned. 11 READMEs and tables of contents written. Neurorights map added to root README."},{"date":"2026-02-21","type":"release","title":"Unified QIF API (/api/qif.json)","description":"Single endpoint serving all QIF data: 103 threats, 24 devices, 38 brain regions, 13 physics constraints, scoring specs, timeline. CORS-enabled, open access. ~580 KB."},{"date":"2026-02-21","type":"milestone","title":"BCI Research Hub Launched","description":"Dedicated /research/ section consolidating hardware explorer, limits equation, security guardrails, and API documentation under one roof."},{"date":"2026-02-21","type":"milestone","title":"Automation Registry + Timeline Tracking","description":"30 automations catalogued (10 active workflows, 8 disabled, 6 scripts, 4 planned). Timeline staleness detection automated. Self-updating registry via daily GitHub Action."},{"date":"2026-02-21","type":"release","title":"TARA v1.7: Origin Classification + 6 New Techniques","description":"All 109 techniques tagged with origin provenance (literature, qif_recontextualized, qif_chain_synthesis, qif_theoretical, neuroethics_formalized). Original authors credited. 6 new techniques added (T0104-T0109): 4 from Murcia/Lopez Bernal taxonomy gap, 1 neuromorphic mimicry, 1 data alignment exploitation. 24 new research sources added (165 to 193).","stats_snapshot":{"threat_techniques":109,"research_sources":277,"tara_version":"v1.7"}},{"date":"2026-02-21","type":"release","title":"Neurowall v0.8: RunematePolicy Engine","description":"L3 policy agent replaced 14-line if/else stub with a full rule-stack engine. 5 prioritized rules (critical_niss, high_niss, sustained_anomaly, growth_detected, spectral_peak) with cooldown, sustained window tracking, stimulation suppression, and configurable policy stacks. Gemini-validated (Phase 11).","stats_snapshot":{"neurowall_version":"v0.8"}},{"date":"2026-02-25","type":"milestone","title":"CVE Disclosure Response Received","description":"Coordinated vulnerability disclosure for BCI-adjacent streaming library received initial response. Public references genericized to protect disclosure timeline."},{"date":"2026-02-26","type":"release","title":"NISS v1.1: Cognitive Read/Write Split","description":"Single Cognitive Integrity (CG) metric split into Cognitive Reconnaissance (CR, read-side) and Cognitive Disruption (CD, write-side). Weights normalized: CR=0.5, CD=0.5 preserving 20% cognitive share. Cross-AI validated (Gemini 2.5 Pro, Phase 16). 109 techniques rescored.","stats_snapshot":{"threat_techniques":109}},{"date":"2026-02-26","type":"release","title":"Zenodo Working Paper v1.5 Published","description":"NISS v1.1 CR/CD split, weight normalization, 6 metrics. DOI: 10.5281/zenodo.18784513. All site documentation synced to v1.1."},{"date":"2026-02-27","type":"milestone","title":"FIRST.org CVSS SIG: NISS Accepted for Resources Repo","description":"CVSS SIG Co-Chair Nick Leali confirmed NISS extension discussed by the SIG and offered inclusion in the official CVSS Resources repository (github.com/FIRSTdotorg/cvss-resources). First external standards body recognition of the framework."},{"date":"2026-02-27","type":"milestone","title":"Joined FIRST.org CVSS Special Interest Group","description":"Registered as CVSS SIG member via FIRST portal. Contributing domain expertise on neural interface security scoring. The SIG noted no existing domain experts in BCI security among current members."},{"date":"2026-03-14","type":"release","title":"BCI Security Plugin v1.0: Claude Code Marketplace","description":"Published the first neurotechnology security plugin for any AI coding platform. 6 skills (TARA lookup, NISS scoring, neuromodesty check, threat modeling, code scanning, interactive learning), 2 slash commands (/bci, /bci-scan), 1 threat modeling agent, 1 neural data detection hook. Zero dependencies. Designed by 8-agent Quorum swarm, validated by 4-agent review swarm. Dual-licensed: Apache 2.0 (code), CC BY-SA 4.0 (data). github.com/qinnovates/bci-security","stats_snapshot":{"threat_techniques":161,"tara_version":"v1.7"}},{"date":"2026-03-14","type":"release","title":"TARA v1.7: 135 Techniques with Full Evidence Tiers","description":"Expanded from 109 to 135 techniques across 11 biological domains. Added 26 new techniques (QIF-T0110-T0135) covering neuroendocrine, glial, neurovascular, and receptor domains. 6 evidence tiers (CONFIRMED/EMERGING/DEMONSTRATED/THEORETICAL/PLAUSIBLE/SPECULATIVE). Full FDORA Section 524B regulatory mapping. 21-agent validation swarm with zero fabricated techniques.","stats_snapshot":{"threat_techniques":161,"research_sources":311}},{"date":"2026-03-15","type":"release","title":"TARA v1.8: ICD-10 Field Split + NISS Re-scoring","description":"Separated ICD-10 codes (G/H/R) from DSM-5-TR codes (F) across 38 techniques — 64 codes moved to new icd10 field. Re-scored all 26 new techniques (T0110-T0135) from placeholder vectors to individually assessed NISS vectors. Quorum-validated. 6 PINS flags added. Components block deprecated. Severity distribution: 20 high, 59 medium, 55 low, 1 none.","stats_snapshot":{"threat_techniques":161,"tara_version":"v1.8"}},{"date":"2026-03-15","type":"milestone","title":"Phase 14: Three-Model Tier 3 Validation","description":"17 Tier 3 claims (math, physics, engineering, clinical) validated by Claude Quorum (8 agents), Codex GPT-5.2, and Gemini CLI. 1 claim removed (motor cortex structural override — neuromodesty violation). 1 citation corrected (Shannon 1992 — electrical, not thermal). 8 claims qualified with evidence-based caveats. All 3 models agreed on removal.","stats_snapshot":{"cross_ai_validations":14}},{"date":"2026-03-15","type":"release","title":"Whitepaper v8.0: 8 New Sections Rendered","description":"Sections 12-19 rendered from draft to site: Neural Terminal, Patient Self-Sovereignty, Autonomy Guardrails, Passwordless Security, Neural OS Architecture, Vision Restoration Pipeline, Governance Extended, Research Validation. Plus NISS 5.8-5.9 (sensory weighting, neuroplasticity metric). Derivation log count corrected to 106.","stats_snapshot":{"derivation_log_entries":106}},{"date":"2026-03-15","type":"release","title":"BCI Directory: HuggingFace-Style Device Explorer","description":"New page at /bci/directory/ — 57 companies, 68 devices in filterable card grid. 4-mode lens toggle (Security/Clinical/Market/Research), entity toggle (Company/Device), filter chips, KQL-powered search, dynamic aggregate counters, inline detail expansion.","stats_snapshot":{"bci_devices":68}},{"date":"2026-03-15","type":"milestone","title":"QIF-TRUTH v4.3: Full NISS/TARA Current State","description":"Source of truth updated with computed numbers: 135 techniques, 12 domains, 3 modes, severity 20/59/55/1, neurorights distribution, ICD-10 split documentation, version history table. All numbers from registrar, not estimated."}],"current_stats":{"as_of":"2026-03-15","threat_techniques":161,"bci_devices":68,"brain_regions":37,"physics_constraints":13,"hourglass_bands":11,"tara_tactics":16,"neurorights_mapped":6,"dsm5_diagnoses_mapped":68,"physics_constants_verified":14,"preprint_version":"v1.5","nsp_version":"v0.5","tara_version":"v1.8","atlas_version":"v1.2.0","derivation_log_entries":106,"field_journal_entries":20,"neurowall_version":"v0.8","blog_posts":59,"cross_ai_validations":14,"research_sources":311}}