Riemannian Geometry Applied to BCI Classification

Abstract. In brain computer interface based on motor imagery, covariances matrices are widely used through spatial filters computation and other signal processing methods. Covariances matrices lie in the space of Semi-definite Positives (SPD) matrices and therefore, fall within the Riemannian geometry domain. Using a differential geometry frameworks, we propose different algorithms in order to classify covariances matrices in their native space.

• Type: Journal
• Year: 2010
• Where: ICA
• Authors: Alexandre Barachant, Stéphane Bonnet, Marco Congedo, Christian Jutten
• Keywords: ICA 2010, Riemannian Geometry Domain, Signal Processing, Signal Processing Methods, Spatial Filters Computation