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dc.contributor.authorBhavsar, Ronakben
dc.contributor.authorSun, Yi
dc.contributor.authorHelian, Na
dc.contributor.authorDavey, Neil
dc.contributor.authorMayor, David
dc.contributor.authorSteffert, Tony
dc.date.accessioned2018-03-29T15:59:32Z
dc.date.available2018-03-29T15:59:32Z
dc.date.issued2018-02-03
dc.identifier.citationBhavsar , R , Sun , Y , Helian , N , Davey , N , Mayor , D & Steffert , T 2018 , ' The Correlation between EEG Signals as Measured in Different Positions on Scalp Varying with Distance ' , Procedia Computer Science , vol. 123 , pp. 92-97 . https://doi.org/10.1016/j.procs.2018.01.015
dc.identifier.issn1877-0509
dc.identifier.otherORCID: /0000-0001-6687-0306/work/64003372
dc.identifier.urihttp://hdl.handle.net/2299/19947
dc.description© 2018 The Author(s). Published by Elsevier B. V. This is an open access article under the CC BY-NC-ND license https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.description.abstractBiomedical signals such as electroencephalogram (EEG) are the time varying signal, and different position of electrodes give different time varying signals. There might be a correlation between these signals. It is likely that the correlation is related to the actual position of electrodes. In this paper, we show that correlation is related to the physical distance between electrodes as measured. This finding is independent of participants and brain hemisphere. Our results indicate that the EEG signal is not transmitted via neurons but through white matter in a brain.en
dc.format.extent6
dc.format.extent1163448
dc.language.isoeng
dc.relation.ispartofProcedia Computer Science
dc.subjectBiomedical signal processing
dc.subjectCross-Correlation
dc.subjectEEG
dc.subjectTime Series Data Analysis
dc.subjectComputer Science(all)
dc.titleThe Correlation between EEG Signals as Measured in Different Positions on Scalp Varying with Distanceen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionBiocomputation Research Group
dc.description.statusPeer reviewed
dc.identifier.urlhttp://bica2017.bicasociety.org/wp-content/uploads/2017/08/BICA_2017_paper_102.pdf
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85045611955&partnerID=8YFLogxK
rioxxterms.versionofrecord10.1016/j.procs.2018.01.015
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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