 |
Conference papers
BCI Signal Classification using a Riemannian-based kernel
Abstract : 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.
https://hal.archives-ouvertes.fr/hal-00693321
Contributor : Alexandre Barachant <>
Submitted on : Wednesday, May 2, 2012 - 2:02:34 PM
Last modification on : Wednesday, May 13, 2020 - 4:30:05 PM
Document(s) archivé(s) le : Friday, August 3, 2012 - 2:43:32 AM
Contributor : Alexandre Barachant <>
Submitted on : Wednesday, May 2, 2012 - 2:02:34 PM
Last modification on : Wednesday, May 13, 2020 - 4:30:05 PM
Document(s) archivé(s) le : Friday, August 3, 2012 - 2:43:32 AM
