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Dimensionality Reduction for BCI classification using Riemannian geometry

Abstract : In the past few years, there has been an increasing interest among the Brain-Computer Interface research community in classification algorithms that respect the intrinsic geometry of covariance matrices. These methods are based on concepts of Riemannian geometry and, despite demonstrating good performances on several occasions, do not scale well when the number of electrodes increases. In this paper, we evaluate two methods for reducing the dimension of the covariance matrices in a geometry-aware fashion. Our results on three different datasets show that it is possible to considerably reduce the dimension of covariance matrices without losing classification power.
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Contributor : Pedro Luiz Coelho Rodrigues <>
Submitted on : Thursday, September 21, 2017 - 10:46:56 AM
Last modification on : Wednesday, May 19, 2021 - 3:58:47 PM

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Pedro Luiz Coelho Rodrigues, Florent Bouchard, Marco Congedo, Christian Jutten. Dimensionality Reduction for BCI classification using Riemannian geometry. BCI 2017 - 7th International Brain-Computer Interface Conference, Gernot R. Müller-Putz, Sep 2017, Graz, Austria. ⟨10.3217/978-3-85125-533-1-16⟩. ⟨hal-01591258⟩

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