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

Camille No\^us; Florian Yger; Marie-Constance Corsi; Sylvain Chevallier

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

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arxiv 2102.06015 v2 pith:AXQYPPDQ submitted 2021-02-09 eess.SP cs.LG

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

Marie-Constance Corsi , Florian Yger , Sylvain Chevallier , Camille No\^us This is my paper

classification eess.SP cs.LG

keywords approachclinicalconnectivitygeometryriemanniantaskaimsapplication

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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.

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

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