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

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      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.

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      Most cited references 56

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      An internally or externally paced event results not only in the generation of an event-related potential (ERP) but also in a change in the ongoing EEG/MEG in form of an event-related desynchronization (ERD) or event-related synchronization (ERS). The ERP on the one side and the ERD/ERS on the other side are different responses of neuronal structures in the brain. While the former is phase-locked, the latter is not phase-locked to the event. The most important difference between both phenomena is that the ERD/ERS is highly frequency band-specific, whereby either the same or different locations on the scalp can display ERD and ERS simultaneously. Quantification of ERD/ERS in time and space is demonstrated on data from a number of movement experiments.
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        Evidence is presented that EEG oscillations in the alpha and theta band reflect cognitive and memory performance in particular. Good performance is related to two types of EEG phenomena (i) a tonic increase in alpha but a decrease in theta power, and (ii) a large phasic (event-related) decrease in alpha but increase in theta, depending on the type of memory demands. Because alpha frequency shows large interindividual differences which are related to age and memory performance, this double dissociation between alpha vs. theta and tonic vs. phasic changes can be observed only if fixed frequency bands are abandoned. It is suggested to adjust the frequency windows of alpha and theta for each subject by using individual alpha frequency as an anchor point. Based on this procedure, a consistent interpretation of a variety of findings is made possible. As an example, in a similar way as brain volume does, upper alpha power increases (but theta power decreases) from early childhood to adulthood, whereas the opposite holds true for the late part of the lifespan. Alpha power is lowered and theta power enhanced in subjects with a variety of different neurological disorders. Furthermore, after sustained wakefulness and during the transition from waking to sleeping when the ability to respond to external stimuli ceases, upper alpha power decreases, whereas theta increases. Event-related changes indicate that the extent of upper alpha desynchronization is positively correlated with (semantic) long-term memory performance, whereas theta synchronization is positively correlated with the ability to encode new information. The reviewed findings are interpreted on the basis of brain oscillations. It is suggested that the encoding of new information is reflected by theta oscillations in hippocampo-cortical feedback loops, whereas search and retrieval processes in (semantic) long-term memory are reflected by upper alpha oscillations in thalamo-cortical feedback loops. Copyright 1999 Elsevier Science B.V.
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          An information-maximization approach to blind separation and blind deconvolution.

          We derive a new self-organizing learning algorithm that maximizes the information transferred in a network of nonlinear units. The algorithm does not assume any knowledge of the input distributions, and is defined here for the zero-noise limit. Under these conditions, information maximization has extra properties not found in the linear case (Linsker 1989). The nonlinearities in the transfer function are able to pick up higher-order moments of the input distributions and perform something akin to true redundancy reduction between units in the output representation. This enables the network to separate statistically independent components in the inputs: a higher-order generalization of principal components analysis. We apply the network to the source separation (or cocktail party) problem, successfully separating unknown mixtures of up to 10 speakers. We also show that a variant on the network architecture is able to perform blind deconvolution (cancellation of unknown echoes and reverberation in a speech signal). Finally, we derive dependencies of information transfer on time delays. We suggest that information maximization provides a unifying framework for problems in "blind" signal processing.
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            Author and article information

            Affiliations
            1School of Kinesiology, University of Michigan Ann Arbor, MI, USA
            2Department of Mathematics and Statistics, University of Minnesota Duluth Duluth, MN, USA
            3Department of Biomedical Engineering, University of Michigan Ann Arbor, MI, USA
            Author notes

            Edited by: John J. Foxe, University of Rochester Medical Center, USA

            Reviewed by: Richard B. Reilly, Trinity College Dublin, Ireland; Johanna Wagner, Graz University of Technology, Austria; Thomas C. Bulea, National Institutes of Health, USA

            *Correspondence: Kristine L. Snyder klsnyder@ 123456d.umn.edu
            Contributors
            Journal
            Front Hum Neurosci
            Front Hum Neurosci
            Front. Hum. Neurosci.
            Frontiers in Human Neuroscience
            Frontiers Media S.A.
            1662-5161
            01 December 2015
            2015
            : 9
            4664645
            10.3389/fnhum.2015.00639
            Copyright © 2015 Snyder, Kline, Huang and Ferris.

            This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and 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.

            Counts
            Figures: 6, Tables: 2, Equations: 1, References: 64, Pages: 13, Words: 7671
            Funding
            Funded by: U.S. Army Research Laboratory 10.13039/100006754
            Award ID: W911NF-09-1-0139
            Award ID: W911NF-10-2-0022
            Funded by: National Institutes of Health 10.13039/100000002
            Award ID: R01-NS073649
            Funded by: National Science Foundation 10.13039/100000001
            Award ID: DBI-1202720
            Categories
            Neuroscience
            Original Research

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