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Collective Almost Synchronization-based model to extract and predict features of EEG signals

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dc.contributor.authorNguyen, Phuong Thi Mai
dc.contributor.authorHayashi, Yoshikatsu
dc.contributor.authorBaptista, M. S.
dc.contributor.authorKondo, Toshiyuki
dc.contributor.institutionUniversity of Aberdeen.Institute for Complex Systems and Mathematical Biology (ICSMB)en
dc.contributor.institutionUniversity of Aberdeen.Physicsen
dc.contributor.institutionUniversity of Aberdeen.Centre for Energy Transitionen
dc.date.accessioned2020-10-09T07:35:01Z
dc.date.available2020-10-09T07:35:01Z
dc.date.issued2020-10-01
dc.descriptionThis research was supported by JSPS KAKENHI (Grant Numbers: JP17KK0064, JP18K19732, JP19H05727, and JP20H02111) and a research grant from the Institute of Global Innovation Research at Tokyo University of Agriculture and Technology.en
dc.description.statusPeer revieweden
dc.format.extent16
dc.format.extent3859342
dc.identifier176009261
dc.identifier2a7f84ec-0c75-4d54-b122-3c6da52b204d
dc.identifier33004963
dc.identifier85091788972
dc.identifier000577143400105
dc.identifier.citationNguyen, P T M, Hayashi, Y, Baptista, M S & Kondo, T 2020, 'Collective Almost Synchronization-based model to extract and predict features of EEG signals', Scientific Reports, vol. 10, 16342. https://doi.org/10.1038/s41598-020-73346-zen
dc.identifier.doi10.1038/s41598-020-73346-z
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/2164/15215
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85091788972&partnerID=8YFLogxKen
dc.identifier.vol10en
dc.language.isoeng
dc.relation.ispartofScientific Reportsen
dc.subjectBiophysical modelsen
dc.subjectComputational modelsen
dc.subjectComputational neuroscienceen
dc.subjectHURST EXPONENTen
dc.subjectNETWORKSen
dc.subjectBRAIN DYNAMICSen
dc.subjectQC Physicsen
dc.subjectQA Mathematicsen
dc.subjectGeneralen
dc.subjectSupplementary Dataen
dc.subject.lccQCen
dc.subject.lccQAen
dc.titleCollective Almost Synchronization-based model to extract and predict features of EEG signalsen
dc.typeJournal articleen

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