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

Alexandre Barachant (1, 2) , Stéphane Bonnet (2) , Marco Congedo (1) , Christian Jutten (1)

1 GIPSA-VIBS - GIPSA - Vision and Brain Signal Processing
2 CEA-LETI - Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information

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.

Mots clés: Brain-Computer Interfaces; Covariance Matrix; Kernel; Support Vector Machine; Riemannian Geometry

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

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⟩