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

© The Institution of Engineering and Technology
Received 29/05/2013, Published
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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.

Inspec keywords: matrix algebra; medical signal processing; data analysis; signal classification; feature extraction; principal component analysis; magnetoencephalography

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

Subjects: Other topics in statistics; Algebra; Algebra, set theory, and graph theory; Biomagnetic signals; Signal processing and detection; Other topics in statistics; Electric and magnetic fields (medical uses); Biology and medical computing; Digital signal processing; Probability theory, stochastic processes, and statistics; Algebra

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