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dc.contributor.authorMahini, Reza
dc.contributor.authorLi, Yansong
dc.contributor.authorDing, Weiyan
dc.contributor.authorFu, Rao
dc.contributor.authorRistaniemi, Tapani
dc.contributor.authorNandi, Asoke K.
dc.contributor.authorChen, Guoliang
dc.contributor.authorCong, Fengyu
dc.date.accessioned2020-10-26T12:14:48Z
dc.date.available2020-10-26T12:14:48Z
dc.date.issued2020
dc.identifier.citationMahini, R., Li, Y., Ding, W., Fu, R., Ristaniemi, T., Nandi, A. K., Chen, G., & Cong, F. (2020). Determination of the Time Window of Event-Related Potential Using Multiple-Set Consensus Clustering. <i>Frontiers in Neuroscience</i>, <i>14</i>, Article 521595. <a href="https://doi.org/10.3389/fnins.2020.521595" target="_blank">https://doi.org/10.3389/fnins.2020.521595</a>
dc.identifier.otherCONVID_42904478
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/72335
dc.description.abstractClustering is a promising tool for grouping the sequence of similar time-points aimed to identify the attention blocks in spatiotemporal event-related potentials (ERPs) analysis. It is most likely to elicit the appropriate time window for ERP of interest if a suitable clustering method is applied to spatiotemporal ERP. However, how to reliably estimate a proper time window from entire individual subjects’ data is still challenging. In this study, we developed a novel multiset consensus clustering method in which several clustering results of multiple subjects were combined to retrieve the best fitted clustering for all the subjects within a group. Then, the obtained clustering was processed by a newly proposed time-window detection method to determine the most suitable time window for identifying the ERP of interest in each condition/group. Applying the proposed method to the simulated ERP data and real data indicated that the brain responses from the individual subjects can be collected to determine a reliable time window for different conditions/groups. Our results revealed more precise time windows to identify N2 and P3 components in the simulated data compared to the state-of-the-art methods. Additionally, our proposed method achieved more robust performance and outperformed statistical analysis results in the real data for N300 and prospective positivity components. To conclude, the proposed method successfully estimates the time window for ERP of interest by processing the individual data, offering new venues for spatiotemporal ERP processing.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherFrontiers Media SA
dc.relation.ispartofseriesFrontiers in Neuroscience
dc.rightsCC BY 4.0
dc.subject.othermulti-set consensus clustering
dc.subject.othertime window
dc.subject.otherevent-related potentials
dc.subject.othermicrostates analysis
dc.subject.othercognitive neuroscience
dc.titleDetermination of the Time Window of Event-Related Potential Using Multiple-Set Consensus Clustering
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202010266385
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.relation.issn1662-4548
dc.relation.volume14
dc.type.versionpublishedVersion
dc.rights.copyright© 2020 Mahini, Li, Ding, Fu, Ristaniemi, Nandi, Chen and Cong
dc.rights.accesslevelopenAccessfi
dc.subject.ysoklusterianalyysi
dc.subject.ysokognitiivinen neurotiede
dc.subject.ysosignaalianalyysi
dc.subject.ysosignaalinkäsittely
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p27558
jyx.subject.urihttp://www.yso.fi/onto/yso/p23133
jyx.subject.urihttp://www.yso.fi/onto/yso/p26805
jyx.subject.urihttp://www.yso.fi/onto/yso/p12266
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.3389/fnins.2020.521595
jyx.fundinginformationThis work was supported by National Natural Science Foundation of China (Grant No. 91748105), National Foundation in China (No. JCKY2019110B009) and the Fundamental Research Funds for the Central Universities (DUT2019) in Dalian University of Technology in China. YL was supported by the National Natural Science Foundation of China (Grant No. 31600929) and the Fundamental Research Funds for the Central Universities (010914380002).


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