RIGOLETTO -- RIemannian GeOmetry LEarning: applicaTion To cOnnectivity. A contribution to the Clinical BCI Challenge -- WCCI2020

Marie-Constance Corsi, Florian Yger, Sylvain Chevallier, Camille Noûs

This short technical report describes the approach submitted to the Clinical BCI Challenge-WCCI2020. This submission aims to classify motor imagery task from EEG signals and relies on Riemannian Geometry, with a twist. Instead of using the classical covariance matrices, we also rely on measures of functional connectivity. Our approach ranked 1st on the task 1 of the competition.

Knowledge Graph



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