Classification of covariance matrices using a Riemannian-based kernel for BCI applications
The use of spatial covariance matrix as a feature is investigated for motor imagery EEG-based classification in braincomputer interface applications. 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, effectively replacing the traditional spatial filtering approach.
kernel covariance matrix braincomputer interfaces riemannian geometry support vector machine