A novel classification strategy of motor imagery EEG signals utilizing WT-PSR-SVD-based MTSVM

In order to control the actions of the devices by the electroencephalogram (EEG) signals accurately, the classification method of motor imagery EEG signals based on wavelet transform (WT)-phase space reconstruction (PSR)-singular value decomposition (SVD)-based multi-layer twin support vector machine (MTSVM) is presented in this study. WT-PSR-SVD which is abbreviated as WPS is presented to obtain the features of the EEG signals, the wavelet decomposed signals (WDSs) of the EEG signals are reconstructed into high-dimensional space by PSR, respectively, and the dynamic characteristics of the WDSs of the EEG signals can be reflected. The traditional multi-layer twin SVM trained by the training samples with the features based on WT-SVD (WS-MTSVM) can be used to compare with the proposed WPS-MTSVM method. The experimental results demonstrate that the classification ability of motor imagery EEG signals of the proposed WPS-MTSVM method is better than that of WSMTSVM. It can be seen that feature extraction of motor imagery EEG signals of WT-PSR-SVD is superior to that of WT-SVD, and the classification method of motor imagery EEG signals of the proposed WPS-MTSVM method is effective.