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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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Contributor : Alexandre Barachant <>
Submitted on : Wednesday, May 2, 2012 - 2:02:34 PM
Last modification on : Friday, April 23, 2021 - 7:56:02 AM
Long-term archiving on: : Friday, August 3, 2012 - 2:43:32 AM


  • HAL Id : hal-00693321, version 1


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



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