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dc.contributor.authorZhang, Guanghui
dc.contributor.authorCarrasco, Carlos D.
dc.contributor.authorWinsler, Kurt
dc.contributor.authorBahle, Brett
dc.contributor.authorCong, Fengyu
dc.contributor.authorLuck, Steven J.
dc.date.accessioned2024-05-15T09:22:16Z
dc.date.available2024-05-15T09:22:16Z
dc.date.issued2024
dc.identifier.citationZhang, G., Carrasco, C. D., Winsler, K., Bahle, B., Cong, F., & Luck, S. J. (2024). Assessing the effectiveness of spatial PCA on SVM-based decoding of EEG data. <i>Neuroimage</i>, <i>293</i>, Article 120625. <a href="https://doi.org/10.1016/j.neuroimage.2024.120625" target="_blank">https://doi.org/10.1016/j.neuroimage.2024.120625</a>
dc.identifier.otherCONVID_213533164
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/94848
dc.description.abstractPrincipal component analysis (PCA) has been widely employed for dimensionality reduction prior to multivariate pattern classification (decoding) in EEG research. The goal of the present study was to provide an evaluation of the effectiveness of PCA on decoding accuracy (using support vector machines) across a broad range of experimental paradigms. We evaluated several different PCA variations, including group-based and subject-based component decomposition and the application of Varimax rotation or no rotation. We also varied the numbers of PCs that were retained for the decoding analysis. We evaluated the resulting decoding accuracy for seven common event-related potential components (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity). We also examined more challenging decoding tasks, including decoding of face identity, facial expression, stimulus location, and stimulus orientation. The datasets also varied in the number and density of electrode sites. Our findings indicated that none of the PCA approaches consistently improved decoding performance related to no PCA, and the application of PCA frequently reduced decoding performance. Researchers should therefore be cautious about using PCA prior to decoding EEG data from similar experimental paradigms, populations, and recording setups.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofseriesNeuroimage
dc.rightsCC BY-NC 4.0
dc.subject.otherEEG
dc.subject.otherMVPA
dc.subject.othergroup-based PCA
dc.subject.othersubject-based PCA
dc.subject.otherdimensionality reduction
dc.titleAssessing the effectiveness of spatial PCA on SVM-based decoding of EEG data
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202405153617
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn1053-8119
dc.relation.volume293
dc.type.versionpublishedVersion
dc.rights.copyright© 2024 The Author(s). Published by Elsevier Inc
dc.rights.accesslevelopenAccessfi
dc.subject.ysopääkomponenttianalyysi
dc.subject.ysosignaalianalyysi
dc.subject.ysoEEG
dc.subject.ysosignaalinkäsittely
dc.subject.ysokoneoppiminen
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p39800
jyx.subject.urihttp://www.yso.fi/onto/yso/p26805
jyx.subject.urihttp://www.yso.fi/onto/yso/p3328
jyx.subject.urihttp://www.yso.fi/onto/yso/p12266
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
dc.rights.urlhttps://creativecommons.org/licenses/by-nc/4.0/
dc.relation.datasethttps://doi.org/10.18115/D5JW4R
dc.relation.doi10.1016/j.neuroimage.2024.120625
jyx.fundinginformationThis study was made possible by grants R01MH087450 and R01 EY033329 from the National Institutes of Health to SJL, USA .
dc.type.okmA1


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