Multiple Mental Thought Parametric Classification: A New Approach for Individual Identification
This paper reports a new approach on identifying the individuality of persons by using parametric classification of multiple mental thoughts. In the approach, ElectroEncephaloGram (EEG) signals were recorded when the subjects were thinking of one or more (up to five) mental thoughts. Autoregressive features were computed from these EEG signals and classified by Linear Discriminant classifier. The results here indicate that near perfect identification of 400 test EEG patterns from four subjects was possible, thereby opening up a new avenue in biometrics.