Feature Extraction Algorithm based on CSP and Wavelet Packet for Motor Imagery EEG signals

Brain-computer interface (BCI) provides a new way for people who arc unable to communicate with each other. The traditional EEG signal feature extraction method based on frequency characteristics only extracts the energy features of each channel, but ignores the correlation information between different channels. In order to obtain better feature extraction results, the method of EEG signal feature extraction based on wavelet packet and Common Space Pattern (CSP) is adopted in this paper. Firstly, on the basis of analyzing channels and frequency bands closely related to event desynchronization, wavelet packet decomposition was carried out for EEG signals to extract the activity imagination EEG co-rhythms and beta rhythms. Spatial filtering was carried out to extract features through the CSP algorithm, and then the related nodes were selected to calculate the wavelet packet energy. Combining the advantages of wavelet packet and CSP method, the correlation information between different channels can be fully utilized, and the Support Vector Machine (SVM) can be used to classify the two kinds of EEG signals. Corresponding experiments were conducted on BCI competition data sets, the classify results show that the proposed feature extraction algorithm can extract useful features for motor imagery EEG signals and get high classify accuracy.