Riemannian Geometry on Connectivity for Clinical BCI
Résumé
Riemannian BCI based on EEG covariance have won many data competitions and achieved very high classification results on BCI datasets. To increase the accuracy of BCI systems, we propose an approach grounded on Riemannian geometry that extends this framework to functional connectivity measures. This paper describes the approach submitted to the Clinical BCI Challenge-WCCI2020 and that ranked 1 st on the task 1 of the competition.
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Poster_ICASSP_HAL.pdf (2.94 Mo)
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corsi2021.pdf (265.71 Ko)
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| Origine | Fichiers produits par l'(les) auteur(s) |
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| Origine | Fichiers produits par l'(les) auteur(s) |
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