Isolating gait-related movement artifacts in electroencephalography during human walking

Julia E. Kline, Helen J. Huang, Kristine L. Snyder, Daniel P. Ferris

Research output: Contribution to journalArticlepeer-review

105 Scopus citations

Abstract

Objective. High-density electroencephelography (EEG) can provide an insight into human brain function during real-world activities with walking. Some recent studies have used EEG to characterize brain activity during walking, but the relative contributions of movement artifact and electrocortical activity have been difficult to quantify. We aimed to characterize movement artifact recorded by EEG electrodes at a range of walking speeds and to test the efficacy of artifact removal methods. We also quantified the similarity between movement artifact recorded by EEG electrodes and a head-mounted accelerometer. Approach. We used a novel experimental method to isolate and record movement artifact with EEG electrodes during walking. We blocked electrophysiological signals using a nonconductive layer (silicone swim cap) and simulated an electrically conductive scalp on top of the swim cap using a wig coated with conductive gel. We recorded motion artifact EEG data from nine young human subjects walking on a treadmill at speeds from 0.4 to 1.6 m s-1. We then tested artifact removal methods including moving average and wavelet-based techniques. Main results. Movement artifact recorded with EEG electrodes varied considerably, across speed, subject, and electrode location. The movement artifact measured with EEG electrodes did not correlate well with head acceleration. All of the tested artifact removal methods attenuated low-frequency noise but did not completely remove movement artifact. The spectral power fluctuations in the movement artifact data resembled data from some previously published studies of EEG during walking. Significance. Our results suggest that EEG data recorded during walking likely contains substantial movement artifact that: cannot be explained by head accelerations; varies across speed, subject, and channel; and cannot be removed using traditional signal processing methods. Future studies should focus on more sophisticated methods for removal of EEG movement artifact to advance the field.

Original languageEnglish (US)
Article number046022
JournalJournal of neural engineering
Volume12
Issue number4
DOIs
StatePublished - Aug 1 2015

Bibliographical note

Publisher Copyright:
© 2015 IOP Publishing Ltd.

Keywords

  • EEG
  • gait
  • movement artifact
  • moving average
  • wavelets

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