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.
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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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Contributeur : Alexandre Barachant <>
Soumis le : mercredi 2 mai 2012 - 14:02:34
Dernière modification le : vendredi 5 février 2016 - 11:14:37
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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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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