This section presents experimental results obtained applying Euclidean and Riemannian distances in SSVEP classification task. The first part of this section ...
Aug 4, 2016 · Co- variance matrices being Symmetric and Positive-Definite (SPD), they are best handled by tools provided by Riemannian geometry.
This applicative contribution aims at assessing the impact of several distances on real EEG dataset , as the invariances embedded in those distances have an ...
This applicative contribution aims at assessing the impact of several distances on real EEG dataset, as the invariances embedded in those distances have an ...
Jun 20, 2019 · Riemannian geometry has been applied to BCI for brain signals classification yielding promising results. Studying electroencephalographic (EEG) ...
This applicative contribution aims at assessing the impact of several distances on real EEG dataset, as the invariances embedded in those distances have an ...
Among the non-parametric means, the Riemannian barycenter with respect to the affineinvariant metric was shown numerically to provide the most accurate ...
From Euclidean to Riemannian Means: Information Geometry for SSVEP Classification ... Authors: Emmanuel K. Kalunga; Sylvain Chevallier; Quentin Barthélemy; Karim ...
Nov 24, 2015 · From Euclidean to Riemannian Means Information Geometry for SSVEP Classification Emmanuel Kalunga, S · Comments.
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Co-authors ; From Euclidean to Riemannian means: Information geometry for SSVEP classification. EK Kalunga, S Chevallier, Q Barthélemy, K Djouani, Y Hamam, ...