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题名: Reactive rhythm activities and offline classification of imagined speeds of finger movements
作者: Fu YF(伏云发) ; Xu BL(徐保磊) ; Pei LL(裴立力) ; Li HY(李洪谊)
作者部门: 机器人学研究室
会议名称: 5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011
会议日期: May 10-12, 2011
会议地点: Wuhan, China
会议主办者: IEEE Engineering in Medicine and Biology Society; Wuhan University; Fuzhou University; Nankai University; Overs. Chin. Sch. Environ. Prot. Assoc. (OCSEPA)
会议录: 5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011
会议录出版者: IEEE Computer Society
会议录出版地: Piscataway, NJ
出版日期: 2011
收录类别: EI
ISBN号: 9781424450893
关键词: Bioinformatics ; Biomedical engineering ; Computer aided diagnosis ; Discriminant analysis ; Fisher information matrix ; Man machine systems ; Network layers ; Neural networks ; Robots ; Spectrum analysis ; Vector quantization
摘要: Reactive rhythm bands to imagined speeds of index finger movement and offline classification of imagined speeds were explored in the paper. EEG was recorded when 4 subjects executed motor imagery of two tasks at first-person perspective that involved left index fingers at two speeds (4Hz and 1Hz, trained and paced by metronome). Relatively prominent rhythmic activities around 10˜13 Hz and 24˜26 Hz over C3, 12˜13 Hz over Cz, and 11˜13 Hz, 8˜10Hz, and 25˜26 Hz over C4 were found by spectrum analysis. The most significant band 9- 13Hz was used to construct the feature space. The Fisher discriminant analysis method (FDA), multi-layer perceptron neural network (MLP), and distinction sensitive learning vector quantization (DSLVQ) were applied in offline identification of imagined speeds respectively. The best average misclassification rates between fast and slow movement imagination by only three derivations C3, Cz, and C4 with FDA, MLP, and DSLVQ were 27.5±1%, 31.2±3.7%, and 34.6% at 3260 ms respectively. The results showed that FDA in this study was better than MLP and DSLVQ. This study demonstrated that discrimination for imagined speeds of finger movement was possible and feasible. Classification of imagined movement speeds with high accuracy based on EEG is also possible through improving methods in the paper. The study may provide a strategy to realize fine control of robots by brain-controlled robot interface.
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内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/8838
Appears in Collections:机器人学研究室_会议论文

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