The removal of EMG artifact from EEG signals by the multivariate empirical mode decomposition

Abstract

The electroencephalogram (EEG) signals were usually contaminated by electromyography (EMG) signals. By using the multivariate empirical mode decomposition (MEMD), we proposed the MEMD-based method to remove EMG artifacts from the EEG signals. Firstly, the EEG signals were decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs) with different frequency bands. Then the power spectra were calculated for every MIMF by using the Welch method. Because the power spectrum of EEG and EMG were focused on different frequency ranges, the MIMFs which included the EMG artifacts could be got rid of. Finally, the clean EEG could be reconstructed by the remaining MIMFs. In this study, the MEMD-based method was used to remove the EMG artifacts for different signal-to-noise ratio (SNR). The experimental results indicated that the SNR of EEG signals could be obviously improved in different conditions and the mean square error (MSE) of EEG signals also could be significantly reduced. In addition, by comparing with the existing artifact removal method > it was demonstrated that the proposed method improved the SNR and reduced the MSE both significantly better than the ICA -based method (p<;0.05).