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.

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.

Figure 1

Conceptual schematic and experimental set up. a) All of the signals that contribute to scalp EEG recordings, categorized as electrophysiological or non-electrophysiological. b) Schematic of the methodological concept for isolating and measuring gait-induced movement artifact in EEG recordings. A silicone swim cap blocks true electrocortical signals while a simulated conductive scalp and a conductive wig allows the electrodes to measure voltage differences resulting from gait dynamics. c) Schematic of the experimental setup. Subjects walked at 4 speeds (0.4, 0.8, 1.2, and 1.6 m/s) on a custom split-belt force measuring treadmill. Trajectories of the calcaneus markers were recorded. An inertial measuring unit (IMU) with a tri-axial accelerometer placed on the forehead above the nose measured accelerations of the head during walking.

Figure 2

Time courses of movement artifact and accelerometer data. Time courses of the ground reaction forces for the right and left legs, head accelerations (vertical, mediolateral, and anterior-posterior), and movement artifacts recorded in 5 electrodes (A1, A19, C18, E12, and G11) for the 4 walking speeds (0.4, 0.8, 1.2, and 1.6 m/s) for a single subject.

Figure 3

Correlations between accelerometer and the frontal electrode movement artifact. a) Vertical head acceleration was plotted against movement artifact recorded in the frontal electrode (E12) for a stride of data for a single subject (same data as in Figure 2). b) Correlation coefficients between all three head accelerations and each electrode recorded movement artifact signals were generally < 0.4. Dark blue represents uncorrelated signals, correlation coefficient = 0.0 and green equals a correlation coefficient of 0.6.

Figure 4

Frequency spectra for the accelerometer and electrode movement artifacts. Frequency spectra of the head accelerations (vertical, mediolateral, and anterior-posterior) differed from the spectra of the electrode recorded movement artifacts (A1, A19, C18, E12, & G11). Each column is a walking speed, increasing from left to right.

Figure 5

ERSP plots for individual subjects for both the head mounted accelerometer and channel A1. ERSP plots for individual subjects at channel A1 reveal inter-subject variability in the movement artifact data. ERSP plots of individual subject accelerometer data show that an accelerometer captures less inter-subject variation. Each row is a subject and each column is a walking speed, increasing from left to right. Red represents a power increase from baseline, and blue represents a power decrease from baseline. The x-axis is one gait cycle: left toe-off (LTO), left heel-strike (LHS), right toe-off (RTO), right heel-strike (RHS), and left toe-off again. Double support occurs between the dotted lines of LHS and RTO and between RHS and LTO. Note the different y axes and color bar power scales.

Figure 6

Group averaged ERSP plots oriented with respect to the head. ERSPs for the head accelerations in all three directions (vertical, mediolateral, and anterior-posterior) showed similar broadband synchronization and desynchronization patterns. ERSPs of the electrode-recorded movement artifact plotted spatially illustrate that midline electrodes (A1, A19, & E12), particularly in the front of the head (E12), were more susceptible to movement artifacts than lateral electrodes (C18 & G11). Red represents a power increase from baseline, and blue represents a power decrease from baseline. Each plot for each electrode is a walking speed, increasing from left to right. The x-axis is one gait cycle: left toe-off (LTO), left heel-strike (LHS), right toe-off (RTO), right heel-strike (RHS), and left toe-off again. Double support occurs between the dotted lines of LHS and RTO and between RHS and LTO.

Figure 7

Group averaged cleaned ERSP plots. Group averaged ERSP plots of the moving average, wavelet, and moving average + wavelet cleaning methods reveal that movement artifact remained despite cleaning. The cleaning results for the least noisy (C18) and most noisy (E12) channels are shown. Each row is a cleaning method and each column is a walking speed, increasing from left to right. Red represents a power increase from baseline, and blue represents a power decrease from baseline. The x-axis is one gait cycle: left toe-off (LTO), left heel-strike (LHS), right toe-off (RTO), right heel-strike (RHS), and left toe-off again. Double support occurs between the dotted lines of LHS and RTO and between RHS and LTO.

Figure 8

Schematic of the setup and ERSP plots for the mastoid experiment. a) To examine the electrode/human skin interface, an electrode was placed on the left mastoid underneath the swim cap. To examine the electrode/simulated scalp interface, the two electrodes closest to the left mastoid, G21 and G22, were used to measure movement artifact from the simulated scalp comprised of the wig coated with conductive gel placed over the swim cap. b) ERSP plots for the mastoid, G21, and G22 electrodes show similar broadband spectral fluctuations between 3–64 Hz.