Hashing the mAR coefficients from EEG data for person authentication

Electroencephalogram (EEG) recordings of brain waves have been shown to have unique pattern for each individual and thus have potential for biometric applications. In this paper, we propose an EEG feature extraction and hashing approach for person authentication. Multi-variate autoregressive (mAR) coefficients are extracted as features from multiple EEG channels and then hashed by using our recently proposed fast Johnson-Lindenstrauss transform (FJLT)-based hashing algorithm to obtain compact hash vectors. Based on the EEG hash vectors, a naive Bayes probabilistic model is employed for person authentication. Our EEG hashing approach presents a fundamental departure from existing methods in EEG-biometry study. The promising results suggest that hashing may open new research directions and applications in the emerging EEG-based biometry area.