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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

Cognitive event related spectral perturbations. Spectral perturbations for subjects performing a Brooks spatial memory task show much smaller fluctuations within a smaller frequency band than the artifact data. Data was epoched around the stimulus (number) presentation, with solid vertical lines indicating the time the stimulus was presented.
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Figure 6: Cognitive event related spectral perturbations. Spectral perturbations for subjects performing a Brooks spatial memory task show much smaller fluctuations within a smaller frequency band than the artifact data. Data was epoched around the stimulus (number) presentation, with solid vertical lines indicating the time the stimulus was presented.

Mentions: Analysis of power spectra and ERSPs clearly distinguished the cortically located movement artifact components as non-neural despite their neural locations (Figures 4–6). Their power spectra revealed artifact at low frequencies that was particularly identifiable for a normal walking speed, 1.2 m/s, and a fast walking speed, 1.6 m/s (Figure 4). Further, for all speeds, the ERSPs for these components generally demonstrated broadband synchronization and desynchronization patterns that are consistent with movement artifact (Figure 5) rather than cognitive changes (Figure 6). These broadband changes made gait-related artifact components identifiable for all speeds. Components from walking data that exhibit these spectra and ERSP patterns should be identified and excluded from any neural analysis.


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)

Cognitive event related spectral perturbations. Spectral perturbations for subjects performing a Brooks spatial memory task show much smaller fluctuations within a smaller frequency band than the artifact data. Data was epoched around the stimulus (number) presentation, with solid vertical lines indicating the time the stimulus was presented.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 6: Cognitive event related spectral perturbations. Spectral perturbations for subjects performing a Brooks spatial memory task show much smaller fluctuations within a smaller frequency band than the artifact data. Data was epoched around the stimulus (number) presentation, with solid vertical lines indicating the time the stimulus was presented.
Mentions: Analysis of power spectra and ERSPs clearly distinguished the cortically located movement artifact components as non-neural despite their neural locations (Figures 4–6). Their power spectra revealed artifact at low frequencies that was particularly identifiable for a normal walking speed, 1.2 m/s, and a fast walking speed, 1.6 m/s (Figure 4). Further, for all speeds, the ERSPs for these components generally demonstrated broadband synchronization and desynchronization patterns that are consistent with movement artifact (Figure 5) rather than cognitive changes (Figure 6). These broadband changes made gait-related artifact components identifiable for all speeds. Components from walking data that exhibit these spectra and ERSP patterns should be identified and excluded from any neural analysis.

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