BCI Signal Classification using a Riemannian-based kernel
Résumé
The use of spatial covariance matrix as feature is investigated for motor imagery EEG-based classification. A new kernel is derived by establishing a connection with the Riemannian geometry of symmetric positive definite matrices. Different kernels are tested, in combination with support vector machines, on a past BCI competition dataset. We demonstrate that this new approach outperforms significantly state of the art results without the need for spatial filtering.
| Origine | Fichiers produits par l'(les) auteur(s) |
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| Format | Autre |
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