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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 - Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information
2 GIPSA-VIBS - GIPSA - Vision and Brain Signal Processing
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
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Conference papers
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https://hal.archives-ouvertes.fr/hal-00693321
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

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  • HAL Id : hal-00693321, version 1

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CEA | CNRS | GIPSA-DIS | GIPSA | DRT | LETI | CEA-GRE | POLYTECH-GRENOBLE | UGA | GIPSA-VIBS

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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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