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

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