Adding Neck Muscle Activity to a Head Phantom Device to Validate Mobile EEG Muscle and Motion Artifact Removal

Abstract

Recent advancement in electroencephalography (EEG) signal processing and hardware can greatly reduce motion artifact, but neck muscle electrical activity is a problem during mobile brain imaging studies examining whole body movement tasks like walking and running. To test the ability of independent component analysis (ICA) to extract neural signals contaminated by neck muscle electrical activity, we broadcast ground-truth electrical signals through a head phantom device during motion. We placed the phantom on a motion platform used to replicate human head trajectories during walking and embedded neck muscle sources within the phantom. ICA was able to extract artificial neural sources from even the most contaminated data in this simulation of human walking. Performance of ICA in high muscle activity amplitude conditions was improved by including electromyographic recordings in the ICA decomposition. These results highlight the importance of recording multiple electromyographic signals from the neck during mobile brain imaging with EEG for studying electrocortical dynamics during movement.