Familiar/unfamiliar face classification from EEG signals by utilizing pairwise distant channels and distinctive time interval

Abstract

The aim of the study is to classify single trial electroencephalogram and to estimate active regions/locations on skull in unfamiliar/familiar face recognition task. For this purpose, electroencephalographic signals were acquired from ten subjects in different sessions. Sixty-one familiar and fifty-nine unfamiliar face stimuli were shown to the subjects in the experiments. Since channel responses are different for familiar and unfamiliar classes, the channels discriminating the classes were investigated. To do so, three distances and four similarity measures were employed to assess the most distant channel pairs between familiar and unfamiliar classes for a 1-s time duration; 0.6 s from the stimulus to 1.6 s in a channel selection process. It is experimentally observed that this time interval is maintaining the greatest distance between two categories. The electroencephalographic signals were classified using the determined channels and time interval to measure accuracy. The best classification accuracy was 81.30% and was obtained with the Pearson correlation as channel selection method. The most discriminative channel pairs were selected from prefrontal regions.