EEG feature extraction and pattern recognition during right and left hands motor imagery in brain-computer interface

At present, it is an important issue to obtain accurate classification of EEG signal in brain-computer interface. We collected the EEG data which are recorded at a sampling rate of 250Hz by EGI-64 scalp electrodes placed according to the international 10/20 system. Firstly, discrete wavelet transform method is used to decompose the average power of the channel C3/C4 and P3/P4 in left and right hands imagine movement during the some experimental period of time. The reconstructed signal of approximation coefficient A6 on the sixth level is selected to build up a feature signal. Secondly, the performances by Fisher Linear Discriminant Analysis and Support Vector Machine methods are compared. The final classifications results show that correct classification rate by Support Vector Machine is higher and gain an ideal classification results, which can establish a basis of real-time BCI application.