Motor imagery electroencephalogram de-noising method based on EEMD and improved wavelet threshold

【摘要】：In order to eliminate the noise mixed in Motor Imagery Electroencephalogram(MI EEG) and retain useful MI EEG information, the paper puts forward a new MI EEG de-noising method based on ensemble empirical mode decomposition(EEMD) and improved wavelet threshold method. New threshold function and threshold selection rules are introduced to the improved wavelet threshold de-noising method. Firstly, the MI EEG signal is decomposed by the EEMD. Then using the improved wavelet threshold method to de-noise the high-frequency Intrinsic Mode Function(IMF) components. Finally, the processed high frequency IMF components and low frequency IMF components are reconstructed to get the de-noised signal. The experimental results reveal that the proposed de-noising algorithm has perspective of higher SNR and lower RMSE compared to the other methods, including the pure EEMD, the pure improved wavelet threshold method, and the improved wavelet threshold method based on EMD.