Model Building Based on Riemannian Geometry

Classification methods, usually based on a definition of distance between two points. When representing the information we want to classify as covariance matrices, using the definition of the Riemannian distance instead of the Euclidean, produces higher success rates.

When classifying these EEG signals from the brain, even when using Riemann geometry, it is still difficult to classify new information that comes from someone we do not have prior information about.

Using calibration methods, as well as innovative lowering dimensions methods, the results are significantly improved, and success rates are quite high.

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Model Building Based on Riemannian Geometry
Model Building Based on Riemannian Geometry