The Study On Muscle Artifacts Removal From Few-Channel EEG Recordings

Electroencephalography(EEG)is a device that records the electrophysiological activity of brain cells.Due to its high temporal resolution,portable and non-invasive characteristics,EEG has been widely used in brain disease diagnosis,brain characteristics research and brain-computer interface applications.However EEG signals,as a weak electrophysiological signal,are often contaminated by electromyogram(EMG),electrocardiogram(ECG),and electrooculogram(EOG)artifacts,which obscure the EEG recordings and make the subsequent analysis difficult.Due to its characteristics of large amplitude,wide frequency spectrum and broad anatomical distribution,EMG artifacts is the most difficult to remove.In the literature,a number of methods have been proposed to deal with this problem.Yet most denoising muscle artifact methods are designed for either single-channel EEG or hospital-based,high-density multichannel recordings,not the few-channel scenario seen in most ambulatory EEG instruments.In this paper,we propose utilizing interchannel dependence information seen in the few-channel situation by combining multivariate empirical mode decomposition and canonical correlation analysis(MEMD-CCA).The proposed method,called MEMD-CCA,first utilizes MEMD to jointly decompose the few-channel EEG recordings into multivariate intrinsic mode functions(IMFs).Then,CCA is applied to further decompose the reorganized multivariate IMFs into the underlying sources.Reconstructing the data using only artifact-free sources leads to artifact-attenuated EEG.We evaluated the performance of the proposed method through simulated,semi-simulated,and real data.The results demonstrated that MEMD-CCA not only effectively eliminated EMG artifacts from the EEG recordings,but also preserved useful brain electrical activity.The proposed method is a promising tool for muscle artifact removal in the few-channel setting.