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Utilizing individual alpha frequency and delta band power in EEG based biometric recognition

Brain activities are inherently determined by a person's unique pattern of neural pathways and are closely associated with his/her genetic personality traits. Brain activity recorded by electroencephalogram (EEG), has recently been regarded as potential candidate in future generation biometric systems. In this paper, a biometric identification system is proposed, combining subject-specific alpha peak frequency, peak power and delta band power values to form discriminative feature vectors and templates. A public dataset of EEG signals recorded from 109 healthy subjects during eyes open/closed (EO/EC) relaxed rest states, has been analyzed here. Using simple similarity measurements based on correlation and distance measures of the test EEG sequences from the template vectors, an average recognition rate of up to 90 % is achieved, by combining spectral features from delta and alpha bands extracted from selected 19 EEG channels. Experimental results explicitly show the usefulness of combining subject-specific alpha and delta bands in future biometric recognition systems. Further investigation is essential to precisely analyze the system and to improve recognition accuracy.