Abstract
Motion and muscle artifacts can undermine signal quality in electroencephalography (EEG) recordings during locomotion. We evaluated approaches for recovering ground-truth artificial brain signals from noisy EEG recordings. We built an electrical head phantom that broadcast four brain and four muscle sources. Head movements were generated by a robotic motion platform. We recorded 128-channel dual layer EEG and 8-channel neck electromyography (EMG) from the head phantom during motion. We evaluated ground-truth electrocortical source signal recovery from artifact contaminated data using Independent Component Analysis (ICA) to determine: (1) the number of isolated noise sensor recordings needed to capture and remove motion artifacts, (2) the ability of Artifact Subspace Reconstruction to remove motion and muscle artifacts at contrasting artifact detection thresholds, (3) the number of neck EMG sensor recordings needed to capture and remove muscle artifacts, and (4) the ability of Canonical Correlation Analysis to remove muscle artifacts. We also evaluated source signal recovery by combining the best practices identified in aims 1-4. By including isolated noise and EMG recordings in the ICA decomposition, we more effectively recovered ground-truth artificial brain signals. A reduced subset of 32-noise and 6-EMG channels showed equivalent performance compared to including the complete arrays. Artifact Subspace Reconstruction improved source separation, but this was contingent on muscle activity amplitude. Canonical Correlation Analysis also improved source separation. Merging noise and EMG recordings into the ICA decomposition, with Artifact Subspace Reconstruction and Canonical Correlation Analysis preprocessing, improved source signal recovery. This study expands on previous head phantom experiments by including neck muscle source activity and evaluating artificial electrocortical spectral power fluctuations synchronized with gait events.
Highlights
E LECTROENCEPHALOGRAPHY (EEG) is increasingly recognized as an effective tool for mobile brain imaging because it is portable, lightweight and offers a high temporal resolution [1]
We extracted artificial brain signals from mobile EEG recordings contaminated by simulated motion and muscle artifacts using an electrical head phantom and robotic motion platform. From these mobile EEG data, we evaluated ground-truth electrocortical source recovery using Independent Component Analysis (ICA) to determine: (1) the number of isolated noise sensor recordings needed to capture and remove motion artifacts, (2) the ability of Artifact Subspace Reconstruction to remove motion artifacts at contrasting artifact detection thresholds, (3) the number of neck EMG sensor recordings needed to capture and remove muscle artifacts, and (4) the ability of Canonical Correlation Analysis to remove muscle artifacts
By including isolated noise sensor recordings in the ICA decomposition of high-density mobile EEG data during motion, we saw improved source separation based on comparisons to ground-truth artificial electrocortical spectral power fluctuations (Fig. 2)
Summary
E LECTROENCEPHALOGRAPHY (EEG) is increasingly recognized as an effective tool for mobile brain imaging because it is portable, lightweight and offers a high temporal resolution [1]. There are repeated observations of increased alpha (8-12 Hz) and beta (12-30 Hz) spectral power in sensorimotor [8], [9], [12] and motor [10], [11] areas during double support prior to contralateral limb push off, followed by a decrease during swing of the contralateral leg These observations have been possible thanks to advanced processing methods that help isolate and remove artifacts. Blind source separation, such as adaptive mixture independent component analysis (AMICA), can separate brain activity from motion and confounding physiological artifacts [13].
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