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Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems
Xu BL(徐保磊); Fu YF(伏云发); Shi G(石刚); Yin XX(尹旭贤); Wang ZD(王志东); Li HY(李洪谊); Jiang ZH(蒋长好)
作者部门机器人学研究室
关键词Brain-computer-interface
发表期刊SCIENTIFIC WORLD JOURNAL
ISSN1537-744X
2014
卷号2014页码:1-10
收录类别SCI
WOS记录号WOS:000343511600001
产权排序1
资助机构National High Technology Research and Development Program of China (863 Program) [2012AA02A605]; National Natural Science Foundation of China (NNSFC) [61203368, 61102014]
摘要We introduce a new motor parameter imagery paradigm using clench speed and clench force motor imagery. The time-frequency-phase features are extracted from mu rhythm and beta rhythms, and the features are optimized using three process methods: no-scaled feature using "MIFS" feature selection criterion, scaled feature using "MIFS" feature selection criterion, and scaled feature using "mRMR" feature selection criterion. Support vector machines (SVMs) and extreme learning machines (ELMs) are compared for classification between clench speed and clench force motor imagery using the optimized feature. Our results show that no significant difference in the classification rate between SVMs and ELMs is found. The scaled feature combinations can get higher classification accuracy than the no-scaled feature combinations at significant level of 0.01, and the "mRMR" feature selection criterion can get higher classification rate than the "MIFS" feature selection criterion at significant level of 0.01. The time-frequency-phase feature can improve the classification rate by about 20% more than the time-frequency feature, and the best classification rate between clench speed motor imagery and clench force motor imagery is 92%. In conclusion, the motor parameter imagery paradigm has the potential to increase the direct control commands for BCI control and the time-frequency-phase feature has the ability to improve BCI classification accuracy.
语种英语
WOS标题词Science & Technology
WOS类目Multidisciplinary Sciences
关键词[WOS]BRAIN-COMPUTER-INTERFACE ; EXTREME LEARNING-MACHINE ; SINGLE-TRIAL EEG ; MOTOR IMAGERY ; MUTUAL INFORMATION ; FEATURE-SELECTION ; BAND IDENTIFICATION ; ADAPTIVE ESTIMATION ; CLASSIFICATION ; PATTERNS
WOS研究方向Science & Technology - Other Topics
引用统计
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/15279
专题机器人学研究室
通讯作者Xu BL(徐保磊)
作者单位1.State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming 650500, China
4.Department of Advanced Robotics, Chiba Institute of Technology, Chiba 2750016, Japan
5.School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110004, China
6.Key Laboratory of Motor and Brain Imaging, Capital Institute of Physical Education, Beijing 100088, China
推荐引用方式
GB/T 7714
Xu BL,Fu YF,Shi G,et al. Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems[J]. SCIENTIFIC WORLD JOURNAL,2014,2014:1-10.
APA Xu BL.,Fu YF.,Shi G.,Yin XX.,Wang ZD.,...&Jiang ZH.(2014).Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems.SCIENTIFIC WORLD JOURNAL,2014,1-10.
MLA Xu BL,et al."Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems".SCIENTIFIC WORLD JOURNAL 2014(2014):1-10.
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