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      A Channel Rejection Method for Attenuating Motion-Related Artifacts in EEG Recordings during Walking

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          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.

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          Most cited references19

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          Independent EEG Sources Are Dipolar

          Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition ‘dipolarity’ defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison).
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            Removal of movement artifact from high-density EEG recorded during walking and running.

            Although human cognition often occurs during dynamic motor actions, most studies of human brain dynamics examine subjects in static seated or prone conditions. EEG signals have historically been considered to be too noise prone to allow recording of brain dynamics during human locomotion. Here we applied a channel-based artifact template regression procedure and a subsequent spatial filtering approach to remove gait-related movement artifact from EEG signals recorded during walking and running. We first used stride time warping to remove gait artifact from high-density EEG recorded during a visual oddball discrimination task performed while walking and running. Next, we applied infomax independent component analysis (ICA) to parse the channel-based noise reduced EEG signals into maximally independent components (ICs) and then performed component-based template regression. Applying channel-based or channel-based plus component-based artifact rejection significantly reduced EEG spectral power in the 1.5- to 8.5-Hz frequency range during walking and running. In walking conditions, gait-related artifact was insubstantial: event-related potentials (ERPs), which were nearly identical to visual oddball discrimination events while standing, were visible before and after applying noise reduction. In the running condition, gait-related artifact severely compromised the EEG signals: stable average ERP time-courses of IC processes were only detectable after artifact removal. These findings show that high-density EEG can be used to study brain dynamics during whole body movements and that mechanical artifact from rhythmic gait events may be minimized using a template regression procedure.
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              Visual Evoked Responses During Standing and Walking

              Human cognition has been shaped both by our body structure and by its complex interactions with its environment. Our cognition is thus inextricably linked to our own and others’ motor behavior. To model brain activity associated with natural cognition, we propose recording the concurrent brain dynamics and body movements of human subjects performing normal actions. Here we tested the feasibility of such a mobile brain/body (MoBI) imaging approach by recording high-density electroencephalographic (EEG) activity and body movements of subjects standing or walking on a treadmill while performing a visual oddball response task. Independent component analysis of the EEG data revealed visual event-related potentials that during standing, slow walking, and fast walking did not differ across movement conditions, demonstrating the viability of recording brain activity accompanying cognitive processes during whole body movement. Non-invasive and relatively low-cost MoBI studies of normal, motivated actions might improve understanding of interactions between brain and body dynamics leading to more complete biological models of cognition.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                26 April 2017
                2017
                : 11
                : 225
                Affiliations
                [1] 1Human Neuromechanics Laboratory, School of Kinesiology, University of Michigan Ann Arbor, MI, USA
                [2] 2Department of Materials and Production, Aalborg University Aalborg, Denmark
                [3] 3U.S. Army Research Laboratory, Aberdeen Proving Ground Aberdeen, MD, USA
                [4] 4Institute of Cognitive Science, University of Osnabrück Osnabrück, Germany
                [5] 5Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf Hamburg, Germany
                Author notes

                Edited by: Alexandre Gramfort, Télécom ParisTech, France

                Reviewed by: Marco Buiatti, University of Trento, Italy; Arnaud Delorme, Centre de Recherche Cerveau et Cognition, France

                *Correspondence: Anderson S. Oliveira oliveira_dkbr@ 123456hotmail.com

                This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2017.00225
                5405125
                28491016
                82f6e383-3c29-4225-8841-403ab0855378
                Copyright © 2017 Oliveira, Schlink, Hairston, König and Ferris.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 30 September 2016
                : 04 April 2017
                Page count
                Figures: 14, Tables: 0, Equations: 0, References: 32, Pages: 17, Words: 9570
                Funding
                Funded by: Army Research Laboratory 10.13039/100006754
                Award ID: ARL W91 1NF-10-2-0022
                Categories
                Neuroscience
                Original Research

                Neurosciences
                eeg,artifacts,walking,locomotion,signal processing,mobile-brain imaging,ica,eeg cleaning
                Neurosciences
                eeg, artifacts, walking, locomotion, signal processing, mobile-brain imaging, ica, eeg cleaning

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