Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems

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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)
刊名	SCIENTIFIC WORLD JOURNAL
	2014
卷号	2014 页码:1-10
关键词	BRAIN-COMPUTER-INTERFACE
ISSN号	1537-744X
通讯作者	徐保磊
产权排序	1
中文摘要	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.
WOS标题词	Science & Technology
类目[WOS]	Multidisciplinary Sciences
研究领域[WOS]	Science & Technology - Other Topics
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
收录类别	SCI
资助信息	National High Technology Research and Development Program of China (863 Program) [2012AA02A605]; National Natural Science Foundation of China (NNSFC) [61203368, 61102014]
语种	英语
WOS记录号	WOS:000343511600001
公开日期	2014-11-29
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/15279]
专题	沈阳自动化研究所_机器人学研究室
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.