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

Authors:
Alexandre Barachant;StéPhane Bonnet;Marco Congedo;Christian Jutten
Affiliations:
CEA-LETI, MINATEC Campus, 17 rue des Martyrs, F-38054 Grenoble, France and Team ViBS (Vision and Brain Signal Processing), GIPSA-Lab, CNRS, Grenoble Universities, Domaine Universitaire, F-38402 Sa ...;CEA-LETI, MINATEC Campus, 17 rue des Martyrs, F-38054 Grenoble, France;Team ViBS (Vision and Brain Signal Processing), GIPSA-Lab, CNRS, Grenoble Universities, Domaine Universitaire, F-38402 Saint Martin d'Hères, France;Team ViBS (Vision and Brain Signal Processing), GIPSA-Lab, CNRS, Grenoble Universities, Domaine Universitaire, F-38402 Saint Martin d'Hères, France
Venue:
Neurocomputing
Year:
2013

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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.