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题名: Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems
作者: 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 ; EXTREME LEARNING-MACHINE ; SINGLE-TRIAL EEG ; MOTOR IMAGERY ; MUTUAL INFORMATION ; FEATURE-SELECTION ; BAND IDENTIFICATION ; ADAPTIVE ESTIMATION ; CLASSIFICATION ; PATTERNS
刊名: SCIENTIFIC WORLD JOURNAL
ISSN号: 1537-744X
出版日期: 2014
卷号: 2014, 页码:1-10
收录类别: SCI
产权排序: 1
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记录号: WOS:000343511600001
WOS标题词: Science & Technology
类目[WOS]: 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
研究领域[WOS]: Science & Technology - Other Topics
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/15279
Appears in Collections:机器人学研究室_期刊论文

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文件名: Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems.pdf
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