title: Isolating gait-related movement artifacts in electroencephalography during human walking.
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.
  
  
  METHODS
  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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Biomedical Engineering, University of Michigan Ann Arbor, MI, USA.
authors.forename: Julia E
authors.initials: JE
authors.lastname: Kline
authors.forename: Helen J
authors.initials: HJ
authors.lastname: Huang
authors.forename: Kristine L
authors.initials: KL
authors.lastname: Snyder
authors.forename: Daniel P
authors.initials: DP
authors.lastname: Ferris
mesh_terms: D000328:Adult; D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D016138:Walking / Q000502:physiology*; D058067:Wavelet Analysis