A priori-driven multivariate statistical approach to reduce dimensionality of MEG signals

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dc.contributor.authorThomaz C.E.
dc.contributor.authorHall E.L.
dc.contributor.authorMorris P.G.
dc.contributor.authorBowtell R.
dc.contributor.authorBrookes M.J.
dc.contributor.authorGiraldi G.A.
dc.date.accessioned2019-08-19T23:45:25Z
dc.date.available2019-08-19T23:45:25Z
dc.date.issued2013
dc.description.abstractA magnetoencephalography (MEG) multivariate data exploratory analysis is described and implemented that combines the variance criterion used in principal component analysis with some prior knowledge about the sensory experimental task. By using the idea of rearranging the data matrix in classification pairs that correspond to the time-varying representation of either stable or stimulus phases of the specific task, the feature extraction method is constrained reducing significantly the number of principal components necessary to represent most of the total variance explained by the MEG signals. © The Institution of Engineering and Technology 2013.
dc.description.firstpage1123
dc.description.issuenumber18
dc.description.lastpage1124
dc.description.volume49
dc.identifier.citationTHOMAZ, C.E.; BOWTELL, R.; HALL, E.L.; MORRIS, P.G.; BROOKES, M.J.; GIRALDI, G.A.. A priori-driven multivariate statistical approach to reduce dimensionality of MEG signals. Electronics Letters (Online), v. 49, n. 18, p. 1123-1124, 2013.
dc.identifier.doi10.1049/el.2013.1796
dc.identifier.issn0013-5194
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/1269
dc.relation.ispartofElectronics Letters
dc.rightsAcesso Aberto
dc.titleA priori-driven multivariate statistical approach to reduce dimensionality of MEG signals
dc.typeArtigo
fei.scopus.citations1
fei.scopus.eid2-s2.0-84883220338
fei.scopus.subjectExploratory analysis
fei.scopus.subjectFeature extraction methods
fei.scopus.subjectMultivariate data
fei.scopus.subjectMultivariate statistical approaches
fei.scopus.subjectNumber of principal components
fei.scopus.subjectPrior knowledge
fei.scopus.subjectSpecific tasks
fei.scopus.subjectTotal variance
fei.scopus.updated2024-03-04
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84883220338&origin=inward
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