EEG-Based Person Identification Using Rhythmic Brain Activity During Sleep

In this paper we present a novel approach to the person identification problem using rhythmic brain activity of spindles from whole night EEG recordings. The proposed system consists of a feature extraction module and a K-NN based classifier. Different types of features from time, frequency and wavelet domain are used to highlight the topographic, temporal, morphological, spectral and statistical discriminative information of sleep spindles. The feature set's efficacy is exhaustively tested in order to find the most significant descriptors that maximize intra-subject separability. Extensive experiments resulted in the optimal number of sensors and features that must be used to form the subject-specific unique descriptors. The proposed system showed significant identification accuracy of 99% similar to 90% for 2-20 subjects, and not lower than 86% when identifying 28 persons, indicating that this new type of modality should be further investigated to be used in EEG based identification applications.