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

Authors
  • Corsi, Marie-Constance
  • Yger, Florian
  • Chevallier, Sylvain
  • Noûs, Camille
Type
Preprint
Publication Date
Mar 11, 2021
Submission Date
Feb 09, 2021
Source
Biblioteca Digital da Memória Científica do INPE
License
Yellow
External links

Abstract

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.

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