Dimension selection for EEG classification in the SPD Riemannian space based on PSO

Abstract

Several classification methods in the task of electroencephalogram (EEG) classification represent input features as symmetric positive definite (SPD) matrices. By translating the data into a Riemannian space, the classification methods have obtained superior performance; however, the high dimensionality of the data typically results in low computational efficiency and degraded classification performance. Therefore, this paper proposes a novel dimension selection method named dimension selection for SPD matrices on Riemannian manifold (DSSR). DSSR can eliminate redundant dimensions for SPD matrices in the Riemannian space based on the framework of particle swarm optimization (PSO). First, we treat one column of the SPD matrix and its corresponding row as a group, where upon the PSO is used to find optimal groups. Next, we use Riemannian classifiers to evaluate the goodness of the selected groups. Finally, the dimension selected SPD matrices are classified by the generalized learning Riemannian space quantization (GLRSQ). Experimental results on two motor imagery datasets show the superior performance of the proposed methods over EEG classification. Neuroscience experiments prove that the optimal dimensions selected by our proposed method are consistent with the findings of brain science research.