An Efficient and Robust Muscle Artifact Removal Method for Few-Channel EEG

Abstract

The unavoidable muscle artifacts pose challenges on reliable interpretation of the electroencephalogram (EEG) recordings, especially for the wearable few-channel EEG, a new emerging scenario. However, the high computational load and low robustness of the existing methods limit its wider applications and performance in artifact removal. Consequently, we propose an efficient and robust muscle artifact removal approach by jointly employing the Fast Multivariate Empirical Mode Decomposition (FMEMD) and CCA for few-channel EEG. The proposed FMEMD-CCA firstly efficiently decomposes the input EEG recordings into several multivariate Intrinsic Mode Functions (IMF) by applying FMEMD. Secondly, all the multivariate IMFs are processed by CCA for computing the underlying sources. Finally, the sources with low autocorrelations are smartly determined as muscle artifacts and rejected, and therefore the other components are reconstructed for EMG-artifact-free IMFs and EEG. Simulated and real data experiments are carried out for verifying the performance of the proposed method. It takes 10 times less computing time in FMEMD-CCA compared with in Multivariate Empirical Mode Decomposition (MEMD)-CCA for 10-s EEG recordings, using the same computer and software. And the accuracy and the average correlation coefficient are highly consistent in both approaches. Furthermore, in contrast to MEMD-CCA, the proposed FMEMD-CCA works more robustly in low sampling rate based on the real data and benchmark.