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From Euclidean to Riemannian Means: Information Geometry for SSVEP Classification

Abstract : Brain Computer Interfaces (BCI) based on electroencephalog-raphy (EEG) rely on multichannel brain signal processing. Most of the state-of-the-art approaches deal with covariance matrices , and indeed Riemannian geometry has provided a substantial framework for developing new algorithms. Most notably , a straightforward algorithm such as Minimum Distance to Mean yields competitive results when applied with a Riemannian distance. This applicative contribution aims at assessing the impact of several distances on real EEG dataset , as the invariances embedded in those distances have an influence on the classification accuracy . Euclidean and Riemannian distances and means are compared both in term of quality of results and of computational load .
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Contributor : Sylvain Chevallier <>
Submitted on : Thursday, August 4, 2016 - 4:29:28 PM
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Emmanuel Kalunga, Sylvain Chevallier, Quentin Barthélemy, Karim Djouani, Yskandar Hamam, et al.. From Euclidean to Riemannian Means: Information Geometry for SSVEP Classification. Geometric Science of Information, Oct 2015, Palaiseau, France. pp.595-604, ⟨10.1007/978-3-319-25040-3_64⟩. ⟨hal-01351753⟩



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