NDLI: Gaussian mixture modeling in stroke patients' rehabilitation EEG data analysis

Content Provider	IEEE Xplore Digital Library
Author	Hao Zhang Ye Liu Jianyi Liang Jianting Cao Liqing Zhang
Copyright Year	2013
Description	Author affiliation: Saitama Inst. of Technol., Saitama, Japan (Jianting Cao) \|\| Dept. of Comput. Sci. & Eng., Shanghai Jiao Tong Univ., Shanghai, China (Hao Zhang; Ye Liu; Jianyi Liang; Liqing Zhang)
Abstract	Traditional 2-class Motor Imagery (MI) Electroencephalography (EEG) classification approaches like Common Spatial Pattern (CSP) and Support Vector Machine (SVM) usually underperform when processing stroke patients' rehabilitation EEG which are flooded with unknown irregular patterns. In this paper, the classical CSP-SVM schema is improved and a feature learning method based on Gaussian Mixture Model (GMM) is utilized for depicting patients' imagery EEG distribution features. We apply the proposed modeling program in two different modules of our online BCI-FES rehabilitation platform and achieve a relatively higher discrimination accuracy. Sufficient observations and test cases on patients' MI data sets have been implemented for validating the GMM model. The results also reveal some working mechanisms and recovery appearances of impaired cortex during the rehabilitation training period.
Starting Page	2208
Ending Page	2211
File Size	588243
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6609974
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Training Brain models Data models Support vector machines Statistics
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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