EEG Signal Classification Based on a Riemannian Distance Measure

We proposed a k-nearest neighbor EEG signal classification algorithm using a dissimilarity measure defined with a Riemannian distance. The EEG signals are characterized by curves on the manifold of power spectral density matrices. By endowing the manifold with a Riemannian metric we obtain the Riemanian distance between two points on the manifold. Based on this, the measure of dissimilarity is then defined. To best facilitate the classification of similar and dissimilar EEG signal sets, we obtain the optimally weighted Riemannian distance aiming to render signals in different classes more separable while those in the same class more compact. The motivation of the algorithm design and verification method are also provided. Experimental results are presented showing the superior performance of the new metric in comparison to the k-nearest neighbor EEG signal classification algorithm using the commonly used Kullback-Leibler (KL) dissimilarity measure.