Gaussian mixture modeling in stroke patients' rehabilitation EEG data analysis
Gaussian mixture modeling in stroke patients' rehabilitation EEG data analysis
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DOI：
10.1109/embc.2013.6609974
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发表时间：
2013-07
期刊：
影响因子：
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通讯作者：
H. Zhang;Ye Liu;Jianyi Liang;Jianting Cao;Liqing Zhang
中科院分区：
文献类型：
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作者：
H. Zhang;Ye Liu;Jianyi Liang;Jianting Cao;Liqing Zhang
传统的两类运动想象脑电分类方法如公共空间模式（CSP）和支持向量机（SVM）在处理脑卒中患者康复期脑电时往往表现不佳。本文对经典的CSP-SVM模式进行了改进，提出了一种基于高斯混合模型（GMM）的特征学习方法来描述患者脑电的表象分布特征。我们将所提出的建模程序应用于我们的在线BCI-FES康复平台的两个不同模块中，并实现了相对较高的判别准确率。已经对患者的MI数据集进行了充分的观察和测试，以验证GMM模型。研究结果还揭示了康复训练过程中受损皮层的某些工作机制和恢复表现。
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.