Classification of human viewers using high-resolution EEG with SVM

Subject identification and authentication using electroencephalograph (EEG) signals has been gaining interest in the biometric field due to the decreasing prices of EEG systems and the extremely positive results that researchers have seen. Here, we evaluate biometric identification using linear support vector machine (SVM) classification on subjects watching short video clips. In particular, cepstral coefficient feature vectors are formed for each of the 128-channels of our EEG system. We explore the effects on classification of using individual versus grouped channels, different video types, and differing numbers of channels. Furthermore, we also evaluate which regions of the head give the best classification results.