Clinical BCI Challenge-WCCI2020: RIGOLETTO -- RIemannian GeOmetry LEarning, applicaTion To cOnnectivity

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

Corsi, Marie-Constance; Yger, Florian; Chevallier, Sylvain; Noûs, Camille (2021), Clinical BCI Challenge-WCCI2020: RIGOLETTO -- RIemannian GeOmetry LEarning, applicaTion To cOnnectivity. https://basepub.dauphine.psl.eu/handle/123456789/22293

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Type

Rapport

Date

2021

Metadata

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Author(s)

Corsi, Marie-Constance
Yger, Florian
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Chevallier, Sylvain
Noûs, Camille

Abstract (EN)

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.

Subjects / Keywords

Riemannian geometry; Functional connectivity; Ensemble learning; BCI