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

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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https://hal.inria.fr/hal-03139990
Contributor : Marie-Constance Corsi Connect in order to contact the contributor
Submitted on : Friday, February 12, 2021 - 2:11:53 PM
Last modification on : Tuesday, January 11, 2022 - 11:16:07 AM

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  • HAL Id : hal-03139990, version 1
  • ARXIV : 2102.06015

Citation

Marie-Constance Corsi, Florian Yger, Sylvain Chevallier, Camille Noûs. Clinical BCI Challenge-WCCI2020: RIGOLETTO -- RIemannian GeOmetry LEarning, applicaTion To cOnnectivity. [Technical Report] ARAMIS, LAMSADE, LISV. 2021. ⟨hal-03139990⟩

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