Electroencephalogram Signals from Imagined Activities: A Novel Biometric Identifier for a Small Population

Electroencephalogram (EEG) signals extracted during imagined activities have been studied for use in Brain Computer Interface (BCI) applications. The major hurdle in the EEG based BCI is that the EEG signals are unique to each individual. This complicates a universal BCI design. On the contrary, this disadvantage is the advantage when it comes to using EEG signals from imagined activities for biometric applications. Therefore, in this paper, EEG signals from imagined activities are proposed as a biometric to identify the individuality of persons. The approach is based on the classification of EEG signals recorded when a user performs either one or several mental activities (up to five). 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. A total of 108 features (autoregressive coefficients, channel spectral powers, interhemispheric channel spectral power differences and inter-hemispheric channel linear complexity values) are computed from each EEG segment for each mental activity and classified by a linear discriminant classifier using a modified 10 fold cross validation procedure, which gave perfect classification when tested on 500 EEG patterns from five subjects. This initial study has shown the huge potential of the method over existing biometric identification systems as it is impossible to be faked.