BCI Signal Classification using a Riemannian-based kernel

Alexandre Barachant 1, * Stéphane Bonnet 1 Marco Congedo 2 Christian Jutten 2
* Auteur correspondant
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
Type de document :
Communication dans un congrès
20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), Apr 2012, bruges, Belgium. Michel Verleysen, pp.97-102, 2012
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https://hal.archives-ouvertes.fr/hal-00693321
Contributeur : Alexandre Barachant <>
Soumis le : mercredi 2 mai 2012 - 14:02:34
Dernière modification le : mardi 25 septembre 2018 - 01:18:11
Document(s) archivé(s) le : vendredi 3 août 2012 - 02:43:32

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  • HAL Id : hal-00693321, version 1

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CEA | GIPSA-VIBS | GIPSA-DIS | GIPSA | UGA | UGA-TEST-TER | DRT | LETI

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Alexandre Barachant, Stéphane Bonnet, Marco Congedo, Christian Jutten. BCI Signal Classification using a Riemannian-based kernel. 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), Apr 2012, bruges, Belgium. Michel Verleysen, pp.97-102, 2012. 〈hal-00693321〉

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