BCI Signal Classification using a Riemannian-based kernel
Abstract
The use of spatial covariance matrix as feature is investigated for motor imagery EEG-based classification. A new kernel is derived by establishing a connection with the Riemannian geometry of symmetric positive definite matrices. Different kernels are tested, in combination with support vector machines, on a past BCI competition dataset. We demonstrate that this new approach outperforms significantly state of the art results without the need for spatial filtering.
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2012-04-25_ESANN2012_bciSVM.pdf (672.47 Ko)
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| Origin | Files produced by the author(s) |
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| Format | Other |
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