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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)
Department机器人学研究室
Source PublicationSCIENTIFIC WORLD JOURNAL
ISSN1537-744X
2014
Volume2014Pages:1-10
Indexed BySCI
WOS IDWOS:000343511600001
Contribution Rank1
Funding OrganizationNational High Technology Research and Development Program of China (863 Program) [2012AA02A605]; National Natural Science Foundation of China (NNSFC) [61203368, 61102014]
KeywordBrain-computer-interface
AbstractWe 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.
Language英语
WOS HeadingsScience & Technology
WOS SubjectMultidisciplinary Sciences
WOS KeywordBRAIN-COMPUTER-INTERFACE ; EXTREME LEARNING-MACHINE ; SINGLE-TRIAL EEG ; MOTOR IMAGERY ; MUTUAL INFORMATION ; FEATURE-SELECTION ; BAND IDENTIFICATION ; ADAPTIVE ESTIMATION ; CLASSIFICATION ; PATTERNS
WOS Research AreaScience & Technology - Other Topics
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/15279
Collection机器人学研究室
Corresponding AuthorXu BL(徐保磊)
Affiliation1.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
Recommended Citation
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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