Adaptive feature extraction of four-class motor imagery EEG based on best basis of wavelet packet and CSP

This paper investigated the feature extraction of multi-channel four-class motor imagery for electroencephalogram (EEG). A new method which can adaptively extract features on the basis of the best wavelet package basis is proposed to solve the problem such as the low classification accuracy and weak self-adaptation. The traditional distance criterion is optimized which is under the condition that the criteria is additive for the choice of the best wavelet packet basis. And the frequency information is filtered by OVR-CSP algorithm to improve the separability of the feature information in frequency subbands. Simulation results demonstrate that the proposed approach achieve better performance than other common methods.