Classification of EEG signals based on imaginary movement of right and left hand wrist

In this paper we present EEG signal classification used to design Brain-Computer Interface (BCI) system based on imagination of movements of the left and the right hand wrist. A comparative study of two different classifiers has been reported for four different movement of each of the wrist. SVM classifier indicates the average classification accuracy of 94% for the same movement (right Vs left) of wrists and 91% for different movements of both the wrists. An Eigenbrain technique is used for feature extraction and classifies the extracted features by using two different classifiers for achieving best accuracy. The feature reduction here has been performed using Principal Component Analysis (PCA).