Motor Imagery Classification based on Local Isometric Embedding of Riemannian Manifold

Abstract

Since the spatial covariance matrices of EEG lie on a Riemannian manifold, it is potential to exploit Riemannian geometry to decode the motor imagery EEG signal. However, high dimensionality on Riemannian manifold limits the application of existing techniques. In this paper, taking advantage of the locality preserving on Riemannian manifold, we propose a novel dimensionality reduction method, named local isometric embedding (LIE), to learn a low-dimensional embedding from Riemannian manifold. Further with a support vector machine (SVM) classifier performed on the embedding, we obtain a efficient decoding method for motor imagery classification. Experimental evaluation on Dataset IIa of BCI Competition IV reveals that the proposed method outperforms other competing methods.