Riemannian Geometry-Based Spatial Filtering (RSF) Method for Motor Imagery EEG Classification

Motor imagery (MI) is a mental process without actual movement, which can be recognized by electroencephalogram (EEG). Effective pre-processing and feature extraction methods are essential for improving the performance of MI-EEG classification. This paper proposes a novel Riemannian geometry-based spatial filtering (RSF) method, which projects the MI-EEG signals onto a lower-dimensional subspace that maximizes the Riemannian distance between the covariance matrices of different classes. The paper evaluates the performance of RSF on a public dataset (Pan2023) and applies it to several common classification algorithms, such as common spatial pattern (CSP), filter bank CSP (FBCSP), tangent space linear discriminant analysis (TS-LDA), ShallowNet, DeepNet, EEGNet and Graph-CSPNet. The results show that RSF can significantly improve the classification accuracy of these algorithms, and reduce the subsequent computation time cost, especially for those neural network algorithms with complex structures. These results indicate that RSF is an efficient spatial filtering method for MI-EEG classification, and provides a new perspective and method for EEG-based applications.