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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Barachant, A., Bonnet, S., Congedo, M., Jutten, C. (2010). Riemannian Geometry Applied to BCI Classification. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2010. Lecture Notes in Computer Science, vol 6365. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15995-4_78
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DOI: https://doi.org/10.1007/978-3-642-15995-4_78
Publisher Name: Springer, Berlin, Heidelberg
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