Open AccessA priori ‐driven multivariate statistical approach to reduce dimensionality of MEG signals
Author(s) -
Thomaz C.E.,
Hall E.L.,
Giraldi G.A.,
Morris P.G.,
Bowtell R.,
Brookes M.J.
Publication year - 2013
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2013.1796
Subject(s) - principal component analysis , multivariate statistics , magnetoencephalography , pattern recognition (psychology) , a priori and a posteriori , curse of dimensionality , artificial intelligence , computer science , variance (accounting) , dimensionality reduction , feature extraction , exploratory data analysis , mathematics , data mining , machine learning , psychology , electroencephalography , philosophy , accounting , epistemology , psychiatry , business
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.