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

Author

Thomaz, Carlos Eduardo ; Hall, E.L. ; Giraldi, Gilson Antonio ; Morris, P.G. ; Bowtell, R. ; Brookes, M.J.

Author_Institution

Dept. de Eng. Eletr., Centro Univ. da FEI, Sao Bernardo do Campo, Brazil

Volume

49

Issue

18

fYear

2013

fDate

August 29 2013

Firstpage

1123

Lastpage

1124

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.

Keywords

data analysis; feature extraction; magnetoencephalography; matrix algebra; medical signal processing; principal component analysis; signal classification; MEG signals; data matrix; dimensionality reduction; feature extraction method; magnetoencephalography multivariate data exploratory analysis; principal component analysis; priori-driven multivariate statistical approach; sensory experimental task; signal classification pairs; time-varying representation; variance criterion;

fLanguage

English

Journal_Title

Electronics Letters

Publisher

iet

ISSN

0013-5194

Type

jour

DOI

10.1049/el.2013.1796

Filename

6587634