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Conference Papers Year : 2012

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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Dates and versions

hal-00693321 , version 1 (02-05-2012)

Identifiers

  • HAL Id : hal-00693321 , version 1

Cite

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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