BCI Signal Classification using a Riemannian-based kernel

Alexandre Barachant; Stéphane Bonnet; Marco Congedo; Christian Jutten

BCI Signal Classification using a Riemannian-based kernel

Alexandre Barachant ^{1, *} Stéphane Bonnet ¹ Marco Congedo ² Christian Jutten ²

* Corresponding author

1 CEA-LETI - Laboratoire d'Electronique et des Technologies de l'Information

2 GIPSA-VIBS - VIBS

GIPSA-DIS - Département Images et Signal

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.

Keywords : BCI Brain Computer Interface Classification Riemannian Geometry SVM Support Vector Machine

Document type :

Conference papers

Domain :

Computer Science [cs] / Signal and Image Processing

Engineering Sciences [physics] / Signal and Image processing

Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-00693321
Contributor : Alexandre Barachant <>
Submitted on : Wednesday, May 2, 2012 - 2:02:34 PM
Last modification on : Monday, February 25, 2019 - 4:34:19 PM
Document(s) archivé(s) le : Friday, August 3, 2012 - 2:43:32 AM

BCI Signal Classification using a Riemannian-based kernel

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BCI Signal Classification using a Riemannian-based kernel

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