title: Low-Rank Linear Dynamical Systems for Motor Imagery EEG.
abstract: The common spatial pattern (CSP) and other spatiospectral feature extraction methods have become the most effective and successful approaches to solve the problem of motor imagery electroencephalography (MI-EEG) pattern recognition from multichannel neural activity in recent years. However, these methods need a lot of preprocessing and postprocessing such as filtering, demean, and spatiospectral feature fusion, which influence the classification accuracy easily. In this paper, we utilize linear dynamical systems (LDSs) for EEG signals feature extraction and classification. LDSs model has lots of advantages such as simultaneous spatial and temporal feature matrix generation, free of preprocessing or postprocessing, and low cost. Furthermore, a low-rank matrix decomposition approach is introduced to get rid of noise and resting state component in order to improve the robustness of the system. Then, we propose a low-rank LDSs algorithm to decompose feature subspace of LDSs on finite Grassmannian and obtain a better performance. Extensive experiments are carried out on public dataset from "BCI Competition III Dataset IVa" and "BCI Competition IV Database 2a." The results show that our proposed three methods yield higher accuracies compared with prevailing approaches such as CSP and CSSP.
authors.affiliation: The State Key Laboratory of Intelligent Technology and Systems, Computer Science and Technology School, Tsinghua University, FIT Building, Beijing 100084, China; Institute of Medical Equipment, Wandong Road, Hedong District, Tianjin, China.
authors.forename: Wenchang
authors.identifier: 0000-0001-8915-2761
authors.initials: W
authors.lastname: Zhang
authors.affiliation: The State Key Laboratory of Intelligent Technology and Systems, Computer Science and Technology School, Tsinghua University, FIT Building, Beijing 100084, China.
authors.forename: Fuchun
authors.identifier: 0000-0003-3546-6305
authors.initials: F
authors.lastname: Sun
authors.affiliation: The State Key Laboratory of Intelligent Technology and Systems, Computer Science and Technology School, Tsinghua University, FIT Building, Beijing 100084, China.
authors.forename: Chuanqi
authors.initials: C
authors.lastname: Tan
authors.affiliation: The State Key Laboratory of Intelligent Technology and Systems, Computer Science and Technology School, Tsinghua University, FIT Building, Beijing 100084, China.
authors.forename: Shaobo
authors.initials: S
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D058256:Brain Waves / Q000502:physiology*; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008962:Models, Theoretical; D009043:Motor Activity; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated