Multi-domain feature extraction method of motor imagery EEG signal based on DWT and CSP

Abstract

Aiming at the feature extraction of motor imagery electroencephalogram (EEG) signals of four types, this paper proposes a new method combining discrete wavelet transformation (DWT) and common spatial patterns (CSP). First, DWT method is used to select the appropriate frequency band according to the frequency features of signals, and the energy mean of the selected frequency band signal is used as a time-frequency feature. Second, CSP method is proposed to solving double classification problem to solving recognition of four types signals problem and extract spatial features. Finally, fusion features are fed into the support vector machine (SVM) classifier and the classification accuracy reached 72.92%. The result is 6.95% better than using only the CSP method and 12.16% better than using only the DWT method, which verify the effectiveness of the proposed method.