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dc.contributor.authorZhang, Guanghui
dc.contributor.authorZhang, Chi
dc.contributor.authorCao, Shuo
dc.contributor.authorXia, Xue
dc.contributor.authorTan, Xin
dc.contributor.authorSi, Lichengxi
dc.contributor.authorWang, Chenxin
dc.contributor.authorWang, Xiaochun
dc.contributor.authorZhou, Chenglin
dc.contributor.authorRistaniemi, Tapani
dc.contributor.authorCong, Fengyu
dc.date.accessioned2020-01-08T11:55:28Z
dc.date.available2020-01-08T11:55:28Z
dc.date.issued2020
dc.identifier.citationZhang, G., Zhang, C., Cao, S., Xia, X., Tan, X., Si, L., Wang, C., Wang, X., Zhou, C., Ristaniemi, T., & Cong, F. (2020). Multi-domain Features of the Non-phase-locked Component of Interest Extracted from ERP Data by Tensor Decomposition. <i>Brain Topography</i>, <i>33</i>(1), 37-47. <a href="https://doi.org/10.1007/s10548-019-00750-8" target="_blank">https://doi.org/10.1007/s10548-019-00750-8</a>
dc.identifier.otherCONVID_33905529
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/67169
dc.description.abstractThe waveform in the time domain, spectrum in the frequency domain, and topography in the space domain of component(s) of interest are the fundamental indices in neuroscience research. Despite the application of time–frequency analysis (TFA) to extract the temporal and spectral characteristics of non-phase-locked component (NPLC) of interest simultaneously, the statistical results are not always expectedly satisfying, in that the spatial information is not considered. Complex Morlet wavelet transform is widely applied to TFA of event-related-potential (ERP) data, and mother wavelet (which should be firstly defined by center frequency and bandwidth (CFBW) before using the method to TFA of ERP data) influences the time–frequency results. In this study, an optimal set of CFBW was firstly selected from the number sets of CFBW, to further analyze for TFA of the ERP data in a cognitive experiment paradigm of emotion (Anger and Neutral) and task (Go and Nogo). Then tensor decomposition algorithm was introduced to investigate the NPLC of interest from the fourth-order tensor. Compared with the TFA results which only revealed a significant difference between Go and Nogo task condition, the tensor-based analysis showed significant interaction effect between emotion and task. Moreover, significant differences were found in both emotion and task conditions through tensor decomposition. In addition, the statistical results of TFA would be affected by the selected region of interest (ROI), whereas those of the proposed method were not subject to ROI. Hence, this study demonstrated that tensor decomposition method was effective in extracting NPLC, by considering spatial information simultaneously as the potential to explore the brain mechanisms related to experimental design.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofseriesBrain Topography
dc.rightsCC BY 4.0
dc.subject.otherERP
dc.subject.othermother wavelet
dc.subject.othertensor decomposition
dc.subject.othertime-frequency analysis
dc.subject.othernon-phase locked
dc.titleMulti-domain Features of the Non-phase-locked Component of Interest Extracted from ERP Data by Tensor Decomposition
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202001081096
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.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange37-47
dc.relation.issn0896-0267
dc.relation.numberinseries1
dc.relation.volume33
dc.type.versionpublishedVersion
dc.rights.copyright© The Author(s) 2019
dc.rights.accesslevelopenAccessfi
dc.format.contentfulltext
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1007/s10548-019-00750-8
jyx.fundinginformationOpen access funding provided by University of Jyväskylä (JYU). This work was supported by National Natural Science Foundation of China (Grant Nos. 91748105, 81471742, and 61703069) and the Fundamental Research Funds for the Central Universities [DUT2019] in Dalian University of Technology in China, and the scholarships from China Scholarship Council (No. 201806060165).
dc.type.okmA1


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