QIF-T0074

Cognitive inference from longitudinal in-ear EEG (personalized cognitive profiling)

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).