Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-11074
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dc.contributor.authorKlug, Marius-
dc.contributor.authorGramann, Klaus-
dc.date.accessioned2020-12-16T12:35:32Z-
dc.date.available2020-12-16T12:35:32Z-
dc.date.issued2020-10-15-
dc.identifier.issn0953-816X-
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/12199-
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-11074-
dc.description.abstractRecent developments in EEG hardware and analyses approaches allow for recordings in both stationary and mobile settings. Irrespective of the experimental setting, EEG recordings are contaminated with noise that has to be removed before the data can be functionally interpreted. Independent component analysis (ICA) is a commonly used tool to remove artifacts such as eye movement, muscle activity, and external noise from the data and to analyze activity on the level of EEG effective brain sources. The effectiveness of filtering the data is one key preprocessing step to improve the decomposition that has been investigated previously. However, no study thus far compared the different requirements of mobile and stationary experiments regarding the preprocessing for ICA decomposition. We thus evaluated how movement in EEG experiments, the number of channels, and the high‐pass filter cutoff during preprocessing influence the ICA decomposition. We found that for commonly used settings (stationary experiment, 64 channels, 0.5 Hz filter), the ICA results are acceptable. However, high‐pass filters of up to 2 Hz cut‐off frequency should be used in mobile experiments, and more channels require a higher filter to reach an optimal decomposition. Fewer brain ICs were found in mobile experiments, but cleaning the data with ICA has been proved to be important and functional even with low‐density channel setups. Based on the results, we provide guidelines for different experimental settings that improve the ICA decomposition.en
dc.description.sponsorshipTU Berlin, Open-Access-Mittel – 2020en
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc150 Psychologiede
dc.subject.otherartifact removalen
dc.subject.otherelectroencephalogramen
dc.subject.otherindependent component analysisen
dc.subject.othermobile brain/body imagingen
dc.subject.otherpreprocessingen
dc.titleIdentifying key factors for improving ICA‐based decomposition of EEG data in mobile and stationary experimentsen
dc.typeArticleen
dc.date.updated2020-12-07T10:45:14Z-
tub.accessrights.dnbfreeen
tub.publisher.universityorinstitutionTechnische Universität Berlinen
dc.identifier.eissn1460-9568-
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.doi10.1111/ejn.14992en
dcterms.bibliographicCitation.journaltitleEuropean Journal of Neuroscienceen
dcterms.bibliographicCitation.originalpublisherplaceNew York, NYen
dcterms.bibliographicCitation.originalpublishernameWileyen
Appears in Collections:FG Biopsychologie und Neuroergonomie » Publications

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