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Conference Papers Year : 2010

Riemannian geometry applied to BCI classification

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.
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Dates and versions

hal-00602700 , version 1 (23-06-2011)

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Alexandre Barachant, Stephane Bonnet, Marco Congedo, Christian Jutten. Riemannian geometry applied to BCI classification. LVA/ICA 2010 - 9th International Conference on Latent Variable Analysis and Signal Separation, Sep 2010, Saint-Malo, France. pp.629-636, ⟨10.1007/978-3-642-15995-4_78⟩. ⟨hal-00602700⟩
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