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题名: 基于运动相关皮层电位握力运动模式识别研究
其他题名: Recognition of actual grip force movement modes based on movement-related cortical potentials
作者: 伏云发 ; 徐保磊 ; 李永程 ; 李洪谊 ; 王越超 ; 余正涛
作者部门: 机器人学研究室
关键词: 运动相关电位 ; 握力运动模式 ; 支持向量机 ; 脑—机接口 ; 脑—机交互控制 ; 脑控机器人接口
刊名: 自动化学报
ISSN号: 0254-4156
出版日期: 2014
卷号: 40, 期号:6, 页码:1045-1057
收录类别: EI ; CSCD
产权排序: 1
摘要: 面向基于脑{ 机接口(Brain-computer interface, BCI) 的脑{ 机交互控制(Brain-machine interaction control,BMIC) | 直接脑控机器人, 提出一种新的左、右手握力运动参数范式, 在该范式下探索左、右手握力运动相关皮层电位/运动相关电位(Movement-related potentials, MRPs) 的时域特征表示并识别握力运动模式. 在涉及左、右手4 个不同任务的实验中采集了11 个健康被试的脑电信号, 任务期间要求被试以2 种握力变化模式之一完成自愿握力运动, 每种任务随机重复30 次. 不同握力任务之间具有显著差异的运动相关电位特征用于识别握力运动模式. 分别用基于核的Fisher 线性判别分析和支持向量机识别4 个不同的握力运动任务. 研究结果进一步证实运动相关电位可以表征握力运动规划、运动执行和运动监控的脑神经机制过程. 基于核的Fisher 线性判别分析和支持向量机分别获得24§4% 和21§5% 的平均错误分类率. 最小误分类率是12 %, 所有被试平均最小误分类率为20:9 § 5 %. 与传统的仅仅识别参与运动的肢体类型以及识别单侧肢体运动参数的研究相比, 本研究可望为脑{ 机交互控制/脑控机器人接口提供更多的力控制意图指令, 奠定了后续的对比研究基础。
英文摘要: A new paradigm of grip force movement with parameters involving right and left hands is put forward in the study to meet the needs of brain-computer interface based brain-machine interaction control (BMIC) - direct brain-controlled robot interface (BCRI). Time-domain feature representation for grip force movement-related cortical potentials/movement-related potentials (MRPs) and the single-trial recognition of grip force movement modes are explored under the paradigm. EEG signals were picked up from eleven healthy subjects during four different tasks of right and left hands. Subjects were asked to execute voluntary grip movement at two modes of grip force variation. Each task was executed 30 times in a random order repeatedly. The features having significant difference among different grip force tasks are used for the classification of grip force modes by Fisher linear discrimination analysis based on kernel function (k-FLDA) and support vector machine (SVM), respectively. The study further demonstrates that MRPs may reflect brain neural mechanism process for planning, execution and precision of a given grip movement task. The average misclassification rates of 24 ±4% and 21 ±5% across eleven subjects are achieved by k-FLDA and SVM, respectively. The minimum misclassification rate is 12% and the average of minimum misclassification rates across eleven subjects is 20:9 ±5 %. The study is expected to lay a foundation for follow-up comparative researches, which provide some additional force control intention instructions for BMIC/BCRI.
语种: 中文
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/15111
Appears in Collections:机器人学研究室_期刊论文

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