Classification of Motor Imagery Tasks Using Phase Synchronization Analysis of EEG Based on Multivariate Empirical Mode Decomposition

被引：0
|
作者：
Liang, Shuang [1 ,2 ]
Choi, Kup-Sze [3 ]
Qin, Jing [1 ,2 ]
Pang, Wai-Man [4 ]
Heng, Pheng-Ann [1 ,2 ,5 ]
机构：
[1] Chinese Acad Sci, Shenzhen Inst Adv Integrat Technol, Beijing 100864, Peoples R China
[2] Chinese Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China
[3] Hong Kong Polytech Univ, Sch Nursing, Hong Kong, Hong Kong, Peoples R China
[4] Caritas Inst Higher Educ, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China
[5] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China
来源：
2014 4TH IEEE INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND TECHNOLOGY (ICIST) | 2014年
关键词：
Electroencephalogram (EEG); motor imagery (MI); multivariate empirical mode decomposition (MEMD); phase synchronization; brain connectivity;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Phase synchronization has been employed to study brain networks and connectivity patterns. The phase locking value (PLV) is one of the most effective measures widely used for phase synchronization analysis. We first calculate the PLVs of the pair-wise intrinsic mode functions (IMFs) based on multivariate empirical mode decomposition (MEMD) method. Next, the average PLV of the prominent pairs relative to the rest duration is adopted for the classification of motor imagery (MI) tasks. Comparative analysis with the EMD-based PLV method, the proposed method has a significant increase in feature separability for most subjects. This paper demonstrates that MEMD-based PLV method can provide an effective feature in the MI task classification and the potential for BCI applications.
引用
收藏
页码：674 / 677
页数：4
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