Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems
文献类型：期刊论文
| 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)
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| 刊名 | SCIENTIFIC WORLD JOURNAL
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| 出版日期 | 2014 |
| 卷号 | 2014页码:1-10 |
| 关键词 | BRAIN-COMPUTER-INTERFACE |
| ISSN号 | 1537-744X |
| 产权排序 | 1 |
| 通讯作者 | 徐保磊 |
| 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 |
| 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. |
入库方式： OAI收割
来源：沈阳自动化研究所
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