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

Snyder KL, Kline JE, Huang HJ, Ferris DP - Front Hum Neurosci (2015)

Bottom Line: 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.Separating true brain signals from motion artifact could clear the way for EEG brain computer interfaces for assistance during mobile activities, such as walking.

View Article: PubMed Central - PubMed

Affiliation: School of Kinesiology, University of Michigan Ann Arbor, MI, USA ; Department of Mathematics and Statistics, University of Minnesota Duluth Duluth, MN, USA.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Experimental setup and channel location illustration. (A) Illustration of the process of isolating and measuring gait-induced movement artifact in EEG recordings. A simulated conductive scalp permits the electrodes to measure voltage differences resulting from gait dynamics while a silicone swim cap blocks true electrocortical signals. (B) Schematic of experimental setup and channel locations. Subjects walked on a custom split-belt force measuring treadmill at four speeds (0.4, 0.8, 1.2, and 1.6 m/s). Calcaneus marker positions were recorded using motion capture.
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Figure 1: Experimental setup and channel location illustration. (A) Illustration of the process of isolating and measuring gait-induced movement artifact in EEG recordings. A simulated conductive scalp permits the electrodes to measure voltage differences resulting from gait dynamics while a silicone swim cap blocks true electrocortical signals. (B) Schematic of experimental setup and channel locations. Subjects walked on a custom split-belt force measuring treadmill at four speeds (0.4, 0.8, 1.2, and 1.6 m/s). Calcaneus marker positions were recorded using motion capture.

Mentions: To test the effect of semi-periodic movement artifact on ICA and dipole fitting, we devised a novel way to measure only gait-related movement artifact with EEG electrodes (Figure 1). We blocked all real electrophysiological signals and collected only movement artifact with an EEG system while ten healthy subjects walked on a treadmill. We applied ICA to this exclusively movement artifact EEG data. If the combination of ICA and DIPFIT was robust to movement artifact, it should find only sources with non-neural locations and characteristics.


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

Snyder KL, Kline JE, Huang HJ, Ferris DP - Front Hum Neurosci (2015)

Experimental setup and channel location illustration. (A) Illustration of the process of isolating and measuring gait-induced movement artifact in EEG recordings. A simulated conductive scalp permits the electrodes to measure voltage differences resulting from gait dynamics while a silicone swim cap blocks true electrocortical signals. (B) Schematic of experimental setup and channel locations. Subjects walked on a custom split-belt force measuring treadmill at four speeds (0.4, 0.8, 1.2, and 1.6 m/s). Calcaneus marker positions were recorded using motion capture.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4664645&req=5

Figure 1: Experimental setup and channel location illustration. (A) Illustration of the process of isolating and measuring gait-induced movement artifact in EEG recordings. A simulated conductive scalp permits the electrodes to measure voltage differences resulting from gait dynamics while a silicone swim cap blocks true electrocortical signals. (B) Schematic of experimental setup and channel locations. Subjects walked on a custom split-belt force measuring treadmill at four speeds (0.4, 0.8, 1.2, and 1.6 m/s). Calcaneus marker positions were recorded using motion capture.
Mentions: To test the effect of semi-periodic movement artifact on ICA and dipole fitting, we devised a novel way to measure only gait-related movement artifact with EEG electrodes (Figure 1). We blocked all real electrophysiological signals and collected only movement artifact with an EEG system while ten healthy subjects walked on a treadmill. We applied ICA to this exclusively movement artifact EEG data. If the combination of ICA and DIPFIT was robust to movement artifact, it should find only sources with non-neural locations and characteristics.

Bottom Line: 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.Separating true brain signals from motion artifact could clear the way for EEG brain computer interfaces for assistance during mobile activities, such as walking.

View Article: PubMed Central - PubMed

Affiliation: School of Kinesiology, University of Michigan Ann Arbor, MI, USA ; Department of Mathematics and Statistics, University of Minnesota Duluth Duluth, MN, USA.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus