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Journal Articles Neurocomputing Year : 2013

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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Dates and versions

hal-00820475 , version 1 (05-05-2013)

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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, 2013, 112, pp.172-178. ⟨10.1016/j.neucom.2012.12.039⟩. ⟨hal-00820475⟩
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