Wavelet Packet Fusion CWT Algorithm for Motor Imagery EEG Signal Recognition
Aiming at the problems of poor time-frequency resolution and low classification correctness in traditional time-frequency analysis methods for processing motor imagery EEG signals,a feature extraction algorithm based on the wavelet packet transform(WPT)and optimi-zing the continuous wavelet transform(CWT)is proposed.A 4-layer wavelet packet decomposition is performed on the original signal,and significant frequency bands of the event-related synchronization/de-synchronization(ERD/ERS)associated with the execution of motor im-agery are extracted,and then the data in this frequency band are reconstructed as the input data for CWT.CWT is used to get the time-fre-quency features under the optimal time period,and finally the extracted feature set is classified by Support Vector Machine(SVM),compa-ring and verifying the average recognition rate of the single feature and the fusion feature in the whole time period and the optimal time period.In the Data Ⅲ dataset of BCI Competition Ⅱ,the average recognition rate of this method in the optimal time period is 89.04％,and the high-est recognition rate reaches 91.68％,which verifies the effectiveness of the feature extraction algorithm of wavelet packet fusion CWT.
万方数据
维普
