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Computational Intelligence and Neuroscience
Volume 2016, Article ID 2637603, 7 pages
http://dx.doi.org/10.1155/2016/2637603
Research Article

Low-Rank Linear Dynamical Systems for Motor Imagery EEG

1The State Key Laboratory of Intelligent Technology and Systems, Computer Science and Technology School, Tsinghua University, FIT Building, Beijing 100084, China
2Institute of Medical Equipment, Wandong Road, Hedong District, Tianjin, China

Received 18 September 2016; Revised 14 November 2016; Accepted 16 November 2016

Academic Editor: Feng Duan

Copyright © 2016 Wenchang Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.