  Electroencephalographic (EEG) recordings are often contaminated by
electromyographic (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 not
effectively remove most of EMG artifacts. Here, we proposed a modified ICA
model for EMG artifacts removal in the EEG, which is called EMG Removal by
Adding Sources of EMG (ERASE). In this new approach, additional channels of
real EMG from neck and head muscles (reference artifacts) were added as inputs
to ICA in order to "force" the most power from EMG artifacts into a few
independent components (ICs). The ICs containing EMG artifacts (the "artifact
ICs") were identified and rejected using an automated procedure. Simulation
results showed ERASE removed EMG artifacts from EEG significantly more
effectively than conventional ICA. Subsequently, EEG was collected from 8
healthy participants while they moved their hands to test the realistic
efficacy of this approach. Results showed that ERASE successfully removed EMG
artifacts (on average, about 75% of EMG artifacts were removed when using real
EMGs as reference artifacts) while preserving the expected EEG features related
to movement. We also tested the ERASE procedure using simulated EMGs as
reference artifacts (about 63% of EMG artifacts removed). Compared to
conventional ICA, ERASE removed on average 26% more EMG artifacts from EEG.
These results indicate that using additional real or simulated EMG sources can
increase the effectiveness of ICA in removing EMG artifacts from EEG. Combined
with automated artifact IC rejection, ERASE also minimizes potential user bias.
