Clinical BCI Challenge-WCCI2020: RIGOLETTO -- RIemannian GeOmetry LEarning, applicaTion To cOnnectivity - Archive ouverte HAL Access content directly
Reports (Technical Report) Year : 2021

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

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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.

Dates and versions

hal-03139990 , version 1 (12-02-2021)

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Cite

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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