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

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      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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          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.

            Author and article information

            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@
            Front Hum Neurosci
            Front Hum Neurosci
            Front. Hum. Neurosci.
            Frontiers in Human Neuroscience
            Frontiers Media S.A.
            01 December 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.

            Figures: 6, Tables: 2, Equations: 1, References: 64, Pages: 13, Words: 7671
            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
            Original Research


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