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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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Contributor : Alexandre Barachant <>
Submitted on : Thursday, June 23, 2011 - 11:39:07 AM
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Alexandre Barachant, Stephane Bonnet, Marco Congedo, Christian Jutten. Riemannian geometry applied to BCI classification. 9th International Conference Latent Variable Analysis and Signal Separation (LVA/ICA 2010), Sep 2010, Saint-Malo, France. pp.629-636, ⟨10.1007/978-3-642-15995-4_78⟩. ⟨hal-00602700⟩



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