ABSTRACT

Brain computer interface is an emerging area of research where the BCI system is able to detect and interpret the mental activity into computer interpretable signals opening a wide area of applications where activities can be completed without using muscular movement. In Brain Computer Interface research, for classification of EEG signals the raw signals captured has to undergo some preprocessing, to obtain the right attributes for classification. In this paper, we present a system which allows for classification of mental tasks based on a statistical data obtained in frequency domain using Discrete cosine transform and extracting useful frequencies from the same with application of decision tree algorithms for classification.