Riemannian Geometry Applied to BCI Classification (2010)
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1 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.
2 NOTES
This is the classical paper on how to use Riemannian Minimum Distance to Mean (RMDM) and Discriminant Analysis > 2 Fisher’s Geodesic Discriminant Analysis (FGDA).