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

dc.contributor.author	Thomaz C.E.
dc.contributor.author	Hall E.L.
dc.contributor.author	Morris P.G.
dc.contributor.author	Bowtell R.
dc.contributor.author	Brookes M.J.
dc.contributor.author	Giraldi G.A.
dc.date.accessioned	2019-08-19T23:45:25Z
dc.date.available	2019-08-19T23:45:25Z
dc.date.issued	2013
dc.identifier.citation	THOMAZ, 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.issn	0013-5194
dc.identifier.uri	https://repositorio.fei.edu.br/handle/FEI/1269
dc.description.abstract	A 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.relation.ispartof	Electronics Letters
dc.rights	Acesso Restrito
dc.title	A priori-driven multivariate statistical approach to reduce dimensionality of MEG signals
dc.type	Artigo

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