Identifying Individuality Using Mental Task Based Brain Computer Interface

Abstract

In recent years, numerous Brain Computer Interface (BCI) technologies have been developed to assist the disabled. In this paper, mental task based BCI is proposed for a different purpose: to identify the individuality of a person. The idea is based on the classification of electroencephalogram (EEG) signals recorded when a user thinks of either one or two mental tasks. As different individuals have different thought processes, this idea would be appropriate for individual identification. To increase the inter-subject differences, EEG data from six electrodes are used instead of one. Sixth order autoregressive features are computed from EEG signals and classified by Linear Discriminant classifier using a modified 10 fold cross validation procedure, which gave an average error of 0.95% when tested on 400 EEG patterns from four subjects. Though the method would have to undergo further development to obtain repeatable good accuracy; this initial study has shown the huge potential of the method over existing biometric identification systems as it is impossible to be faked.