Independent Component Analysis of Gait-Related Movement Artifact Recorded using EEG Electrodes during Treadmill Walking

• Published in: Frontiers in human neuroscience Vol. 9; p. 639
• Main Authors: Snyder, Kristine L., Kline, Julia E., Huang, Helen J., Ferris, Daniel P.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 01.12.2015; Frontiers Media S.A
• Subjects: Algorithms; artifact removal; blind source separation; Brain mapping; Cortex; cortical sources; EEG; Electrical properties; Electroencephalography; Fitness equipment; Gait; Interfaces; Localization; Medical imaging; Methods; Neuroimaging; Neuroscience; Researchers; Silicones; Spectrum analysis; Tomography; Walking; Ann Arbor Michigan; United States > US; electroencephalography; cortical sources; blind source separation; walking; artifact removal
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2015.00639

There has been a recent surge in the use of electroencephalography (EEG) as a tool for mobile brain imaging due to its portability and fine time resolution. When EEG is combined with independent component analysis (ICA) and source localization techniques, it can model electrocortical activity as arising from temporally independent signals located in spatially distinct cortical areas. However, for mobile tasks, it is not clear how movement artifacts influence ICA and source localization. We devised a novel method to collect pure movement artifact data (devoid of any electrophysiological signals) with a 256-channel EEG system. We first blocked true electrocortical activity using a silicone swim cap. Over the silicone layer, we placed a simulated scalp with electrical properties similar to real human scalp. We collected EEG movement artifact signals from ten healthy, young subjects wearing this setup as they walked on a treadmill at speeds from 0.4-1.6 m/s. We performed ICA and dipole fitting on the EEG movement artifact data to quantify how accurately these methods would identify the artifact signals as non-neural. ICA and dipole fitting accurately localized 99% of the independent components in non-neural locations or lacked dipolar characteristics. The remaining 1% of sources had locations within the brain volume and low residual variances, but had topographical maps, power spectra, time courses, and event related spectral perturbations typical of non-neural sources. Caution should be exercised when interpreting ICA for data that includes semi-periodic artifacts including artifact arising from human walking. Alternative methods are needed for the identification and separation of movement artifact in mobile EEG signals, especially methods that can be performed in real time. Separating true brain signals from motion artifact could clear the way for EEG brain computer interfaces for assistance during mobile activities, such as walking.