Analyse discriminante matricielle descriptive. Application a l'\'etude de signaux EEG

TL;DR

This paper presents a matrix-based linear discriminant analysis method for binary class data, utilizing singular value decomposition under a separability assumption, with application to EEG signals for improved data visualization and discrimination.

Contribution

It introduces a novel descriptive approach to matrix-variate LDA that leverages SVD and Mahalanobis metrics for EEG signal analysis.

Findings

Effective discrimination of EEG signals demonstrated

Data visualization in 2D and 3D plots achieved

Method shows relevance for multi-sensor signal analysis

Abstract

We focus on the descriptive approach to linear discriminant analysis for matrix-variate data in the binary case. Under a separability assumption on row and column variability, the most discriminant linear combinations of rows and columns are determined by the singular value decomposition of the difference of the class-averages with the Mahalanobis metric in the row and column spaces. This approach provides data representations of data in two-dimensional or three-dimensional plots and singles out discriminant components. An application to electroencephalographic multi-sensor signals illustrates the relevance of the method.