Key result
The proposed FMEMD-CCA method efficiently and robustly removed muscle artifacts from few-channel EEG, taking 10 times less computing time than MEMD-CCA while maintaining highly consistent accuracy.
Why the study?
Muscle artifacts hinder reliable EEG interpretation, particularly for wearable few-channel setups, while existing methods suffer from high computational load and low robustness.
The FMEMD-CCA method significantly reduces computation time for muscle artifact removal in few-channel EEG compared to existing methods without compromising accuracy.
May facilitate real-time few-channel EEG monitoring; leaves open prospective clinical validation.
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.
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Dai et al. (2019) studied Muscle artifacts in electroencephalogram (EEG). Fast Multivariate Empirical Mode Decomposition and Canonical Correlation Analysis (FMEMD-CCA) vs. MEMD-CCA and CCA was evaluated on Computation time and artifact removal accuracy (RRMSE, ACC). The proposed FMEMD-CCA method efficiently and robustly removed muscle artifacts from few-channel EEG, taking 10 times less computing time than MEMD-CCA while maintaining highly consistent accuracy.
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