Single-channel techniques may outperform conventional multichannel approaches for removing muscle artifacts from EEG data, particularly at low signal-to-noise ratios.
Single-channel EEG artifact removal may simplify workflows; leaves open validation against multichannel methods in clinical data.
Electroencephalogram (EEG) recordings are often contaminated with muscle artifacts. Muscular activities strongly obscure EEG signals and complicate subsequent EEG-based data analysis. Conventional methods for removing muscle artifact from EEG are usually based on blind source separation techniques and involve jointly analyzing multichannel EEG recordings. Instead of using the multichannel approaches, this paper proposes to explore single-channel techniques for muscle artifact removal from multichannel EEG. It may seem paradoxical that we denoise each channel individually while ignoring interchannel relationships. We conduct a performance comparison study, through numerical simulations and applications to real EEG recordings contaminated with muscle artifacts. The results demonstrate the advantage of single-channel techniques over multichannel ones, especially for low signal-to-noise ratios. This paper may change the traditional understanding of denoising the EEG signals.
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Chen et al. (2015) studied this question.
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