Riemannian geometry applied to BCI classification

Authors:
Alexandre Barachant;Stéphane Bonnet;Marco Congedo;Christian Jutten
Affiliations:
CEA, LETI, DTBS, STD, LE2S, Grenoble, France;CEA, LETI, DTBS, STD, LE2S, Grenoble, France;GIPSA-lab, CNRS, Grenoble Universities, Saint Martin d'Hères, France;GIPSA-lab, CNRS, Grenoble Universities, Saint Martin d'Hères, France
Venue:
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Year:
2010

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Abstract

In brain-computer interfaces based on motor imagery, covariance matrices are widely used through spatial filters computation and other signal processing methods. Covariance matrices lie in the space of Symmetric Positives-Definite (SPD) matrices and therefore, fall within the Riemannian geometry domain. Using a differential geometry framework, we propose different algorithms in order to classify covariance matrices in their native space.