Low-Rank Linear Dynamical Systems for Motor Imagery EEG
Joint Authors
Fu-chun, Sun
Zhang, Wenchang
Tan, Chuanqi
Liu, Shaobo
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-12-21
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhang, Wenchang& Fu-chun, Sun& Tan, Chuanqi& Liu, Shaobo. 2016. Low-Rank Linear Dynamical Systems for Motor Imagery EEG. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1099604
Modern Language Association (MLA)
Zhang, Wenchang…[et al.]. Low-Rank Linear Dynamical Systems for Motor Imagery EEG. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-7.
https://search.emarefa.net/detail/BIM-1099604
American Medical Association (AMA)
Zhang, Wenchang& Fu-chun, Sun& Tan, Chuanqi& Liu, Shaobo. Low-Rank Linear Dynamical Systems for Motor Imagery EEG. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1099604
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099604