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Electroencephalographic (EEG) recordings are often contaminated byelectromyographic (EMG) artifacts, especially when recording during movement.Existing methods to remove EMG artifacts include independent component analysis(ICA), and other high-order statistical methods. However, these methods can noteffectively remove most of EMG artifacts. Here, we proposed a modified ICAmodel for EMG artifacts removal in the EEG, which is called EMG Removal byAdding Sources of EMG (ERASE). In this new approach, additional channels ofreal EMG from neck and head muscles (reference artifacts) were added as inputsto ICA in order to "force" the most power from EMG artifacts into a fewindependent components (ICs). The ICs containing EMG artifacts (the "artifactICs") were identified and rejected using an automated procedure. Simulationresults showed ERASE removed EMG artifacts from EEG significantly moreeffectively than conventional ICA. Subsequently, EEG was collected from 8healthy participants while they moved their hands to test the realisticefficacy of this approach. Results showed that ERASE successfully removed EMGartifacts (on average, about 75% of EMG artifacts were removed when using realEMGs as reference artifacts) while preserving the expected EEG features relatedto movement. We also tested the ERASE procedure using simulated EMGs asreference artifacts (about 63% of EMG artifacts removed). Compared toconventional ICA, ERASE removed on average 26% more EMG artifacts from EEG.These results indicate that using additional real or simulated EMG sources canincrease the effectiveness of ICA in removing EMG artifacts from EEG. Combinedwith automated artifact IC rejection, ERASE also minimizes potential user bias.

Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE) -- A novel ICA-based algorithm for removing myoelectric artifacts from EEG -- Part 1