Classification of covariance matrices using a Riemannian-based kernel for BCI applications

Abstract : The use of spatial covariance matrix as a feature is investigated for motor imagery EEG-based classification in Brain-Computer Interface applications. 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, effectively replacing the traditional spatial filtering approach.
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https://hal.archives-ouvertes.fr/hal-00820475
Contributor : Alexandre Barachant <>
Submitted on : Sunday, May 5, 2013 - 2:04:59 PM
Last modification on : Monday, July 8, 2019 - 3:10:03 PM
Long-term archiving on : Tuesday, April 4, 2017 - 4:45:11 AM

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Alexandre Barachant, Stéphane Bonnet, Marco Congedo, Christian Jutten. Classification of covariance matrices using a Riemannian-based kernel for BCI applications. Neurocomputing, Elsevier, 2013, 112, pp.172-178. ⟨10.1016/j.neucom.2012.12.039⟩. ⟨hal-00820475⟩

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