Common Spatial Pattern revisited by Riemannian Geometry

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

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

This paper presents a link between the well known Common Spatial Pattern (CSP) algorithm and Riemannian geometry in the context of Brain Computer Interface (BCI). It will be shown that CSP spatial filtering and Log variance features extraction can be resumed as a computation of a Riemann distance in the space of covariances matrices. This fact yields to highlight several approximations with respect to the space topology. According to these conclusions, we propose an improvement of classical CSP method.

Mots clés: Riemannian Geometry; Brain Computer Interface

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

Alexandre Barachant, Stephane Bonnet, Marco Congedo, Christian Jutten. Common Spatial Pattern revisited by Riemannian Geometry. MMSP 2010 - IEEE International Workshop on Multimedia Signal Processing, Oct 2010, Saint-Malo, France. pp.472, ⟨10.1109/MMSP.2010.5662067⟩. ⟨hal-00602686⟩