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dc.contributor.authorMahini, Reza
dc.contributor.authorZhang, Guanghui
dc.contributor.authorParviainen, Tiina
dc.contributor.authorDüsing, Rainer
dc.contributor.authorNandi, Asoke K.
dc.contributor.authorCong, Fengyu
dc.contributor.authorHämäläinen, Timo
dc.date.accessioned2024-08-30T07:58:37Z
dc.date.available2024-08-30T07:58:37Z
dc.date.issued2024
dc.identifier.citationMahini, R., Zhang, G., Parviainen, T., Düsing, R., Nandi, A. K., Cong, F., & Hämäläinen, T. (2024). Brain Evoked Response Qualification Using Multi-Set Consensus Clustering : Toward Single-Trial EEG Analysis. <i>Brain Topography</i>, <i>Early online</i>. <a href="https://doi.org/10.1007/s10548-024-01074-y" target="_blank">https://doi.org/10.1007/s10548-024-01074-y</a>
dc.identifier.otherCONVID_233540931
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/96876
dc.description.abstractIn event-related potential (ERP) analysis, it is commonly assumed that individual trials from a subject share similar properties and originate from comparable neural sources, allowing reliable interpretation of group-averages. Nevertheless, traditional group-level ERP analysis methods, including cluster analysis, often overlook critical information about individual subjects’ neural processes due to using fixed measurement intervals derived from averaging. We developed a multi-set consensus clustering pipeline to examine cognitive processes at the individual subject level. Initially, consensus clustering from diverse methods was applied to single-trial EEG epochs of individual subjects. Subsequently, a second level of consensus clustering was performed across the trials of each subject. A newly modified time window determination method was then employed to identify individual subjects’ ERP(s) of interest. We validated our method with simulated data for ERP components N2 and P3, and real data from a visual oddball task to confirm the P3 component. Our findings revealed that estimated time windows for individual subjects provide precise ERP identification compared to fixed time windows across all subjects. Additionally, Monte Carlo simulations with synthetic single-trial data demonstrated stable scores for the N2 and P3 components, confirming the reliability of our method. The proposed method enhances the examination of brain-evoked responses at the individual subject level by considering single-trial EEG data, thereby extracting mutual information relevant to the neural process. This approach offers a significant improvement over conventional ERP analysis, which relies on the averaging mechanism and fixed measurement interval.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer Nature
dc.relation.ispartofseriesBrain Topography
dc.rightsCC BY 4.0
dc.subject.othersingle-trial EEG
dc.subject.othertime window
dc.subject.othermulti-set consensus clustering
dc.subject.otherstandardization
dc.subject.otherEEG/ERP microstates
dc.subject.othercognitive process
dc.titleBrain Evoked Response Qualification Using Multi-Set Consensus Clustering : Toward Single-Trial EEG Analysis
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202408305758
dc.contributor.laitosKasvatustieteiden ja psykologian tiedekuntafi
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Education and Psychologyen
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.issn0896-0267
dc.relation.volumeEarly online
dc.type.versionpublishedVersion
dc.rights.copyright© 2024 the Authors
dc.rights.accesslevelopenAccessfi
dc.subject.ysoaivotutkimus
dc.subject.ysokognitiivinen neurotiede
dc.subject.ysokognitiiviset prosessit
dc.subject.ysoEEG
dc.subject.ysostandardointi
dc.subject.ysokuvantaminen
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p23705
jyx.subject.urihttp://www.yso.fi/onto/yso/p23133
jyx.subject.urihttp://www.yso.fi/onto/yso/p5283
jyx.subject.urihttp://www.yso.fi/onto/yso/p3328
jyx.subject.urihttp://www.yso.fi/onto/yso/p17298
jyx.subject.urihttp://www.yso.fi/onto/yso/p3532
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1007/s10548-024-01074-y
jyx.fundinginformationThe authors have no relevant financial or non-financial interests to disclose. The authors have no funding for this study. Open Access funding provided by University of Jyväskylä (JYU).
dc.type.okmA1


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