P300 Feature Extraction With Wavelet Packet Transform and CSSD

P300 potential is weak and has poor anti-interference ability and low recognition rate. Based on wavelet packet transform(WPT) and common spatial subspace decomposition(CSSD),a feature extraction method,denoted as WPCSSD,was proposed in this paper. First,the EEG was preprocessed by the overlapping average algorithm to improve its signal-to-noise ratio. Second,the EEG was filtered and reconstructed by WPT according to the time-frequency information of P300. Third,the power spectrum based on AR model was computed,and a spatial filter with CSSD was applied to extract the spatial feature of P300. The feature vector can therefore reflect the time-frequency-space information of P300 generally. Finally,the support vector machine was used for classification.Resultsshow that WPCSSD has better anti-interference and adaptive ability,and the recognition accuracy is 95.22% in data sets of BCI competition. The correctness and validity of the method are proven.