Anderson S. Oliveira, Bryan R. Schlink, W. David Hairston, Peter König, Daniel P. Ferris
Abstract
Recording scalp electroencephalography (EEG) during human motion can introduce motion artifacts. Repetitive head movements can generate artifact patterns across scalp EEG sensors. There are many methods for identifying and rejecting bad channels and independent components from EEG datasets, but there is a lack of methods dedicated to evaluate specific intra-channel amplitude patterns for identifying motion-related artifacts. In this study, we proposed a template correlation rejection (TCR) as a novel method for identifying and rejecting EEG channels and independent components carrying motion-related artifacts. We recorded EEG data from 10 subjects during treadmill walking. The template correlation rejection method consists of creating templates of amplitude patterns and determining the fraction of total epochs presenting relevant correlation to the template. For EEG channels, the template correlation rejection removed channels presenting the majority of epochs (>75%) correlated to the template, and presenting pronounced amplitude in comparison to all recorded channels. For independent components, the template correlation rejection removed components presenting the majority of epochs correlated to the template. Evaluation of scalp maps and power spectra confirmed low neural content for the rejected components. We found that channels identified for rejection contained ~60% higher delta power, and had spectral properties locked to the gait phases. After rejecting the identified channels and running independent component analysis on the EEG datasets, the proposed method identified 4.3 ± 1.8 independent components (out of 198 ± 12) with substantive motion-related artifacts. These results indicate that template correlation rejection is an effective method for rejecting EEG channels contaminated with motion-related artifact during human locomotion.
This research output has an Altmetric Attention Score of 16. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 March 2018.
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#2,491,818
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#1,458
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#42,910
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Outputs of similar age from Frontiers in Neuroscience
#22
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Altmetric has tracked 26,787,735 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 12,058 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.4. This one has done well, scoring higher than 87% of its peers.
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We're also able to compare this research output to 209 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.
