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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Barachant_LVA_ICA_2010_final.pdf (1.33 Mo)
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