Cognitive Cryptography Framework for Securing Brain Computer Interfaces Against Neural Signal Hijacking

Brain–Computer Interfaces (BCIs) are rapidly transitioning from experimental prototypes to clinical and consumer deployments, exposing a novel attack surface: neural signal hijacking , wherein adversaries intercept, replay, or inject synthetic electroencephalographic (EEG) streams to compromise prosthetic actuation or infer private cognitive states. Conventional transport-layer security mechanisms fail to address the stochastic, non-stationary, and session-variant characteristics inherent to neural data. To bridge this gap, we introduce the Cognitive Cryptography Framework (CCF) , an end-to-end security architecture that co-designs neurophysiological signal processing with cryptographic primitives for context-aware authenticated encryption. At its core, CCF employs the Oscillation-Bound Frequency-Shift Keyed Hash (OB-FSKH) algorithm, which cryptographically binds epoch-specific session keys to a subject live neural oscillation fingerprint across five canonical frequency bands ( \(\delta\) , \(\theta\) , \(\alpha\) , \(\beta\) , \(\gamma\) ), ensuring automatic key expiration upon cognitive state transitions. Cryptographic entropy is harvested via a Cognitive Biometric Entropy Pool (CBEP) that distills high-fidelity min-entropy from inter-channel phase-locking values and band-power dynamics, seeding a forward-secret HKDF-SHA3-512 double-ratchet that formally guarantees IND-CCA2 confidentiality and EUF-CMA integrity under the random-oracle model. Concurrently, a lightweight Neural Anomaly Detector (NAD) leverages adaptive Mahalanobis distance scoring and Ljung–Box autocorrelation analysis to identify replay and generative injection attacks within a single 250 ms epoch. Extensive empirical validation across two benchmark datasets (BCI Competition IV Dataset 2a and PhysioNet Motor Movement/Imagery) under four adversarial scenarios demonstrates a mean authentication accuracy of 98.7 %, a replay-detection recall of 99.2 %, and an injection-detection \(F_1\) score of 0.987, while incurring only 4.3 ms of deterministic computational overhead per 64-channel epoch on a Raspberry Pi 4 edge node. By unifying neural-context binding, biometric key derivation, and real-time statistical anomaly detection, CCF establishes a clinically viable, low-latency security paradigm for next-generation neural prosthetics, closed-loop neurofeedback systems, and augmented-reality BCI deployments.