基于运动相关皮层电位握力运动模式识别研究

伏云发; 徐保磊; 李永程; 李洪谊; 王越超; 余正涛

doi:10.3724/SP.J.1004.2014.01045

基于运动相关皮层电位握力运动模式识别研究

doi: 10.3724/SP.J.1004.2014.01045

1.
昆明理工大学信息工程与自动化学院昆明 650500;
2.
中国科学院沈阳自动化研究所机器人学国家重点实验室沈阳 110016;
3.
中国科学院大学北京 100049

基金项目:

国家自然科学基金青年基金（60705021），云南省应用基础研究计划项目（2013FB026），云南省级人培项目（KKSY201303048），云南省教育厅重点项目（2013Z130）资助

详细信息

作者简介:
李洪谊中国科学院沈阳自动化研究所研究员. 主要研究方向为医疗机器人系统，机器人遥操作，微小机器人，机器人学与认知科学相结合的新型人机融合技术，非线性控制. E-mail：hli@sia.cn

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出版历程
- 收稿日期: 2012-12-13
- 修回日期: 2013-08-01
- 刊出日期: 2014-06-20

Recognition of Actual Grip Force Movement Modes Based on Movement-related Cortical Potentials

1.
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500;
2.
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences (CAS), Shenyang 110016;
3.
Graduate University of CAS, Beijing 100049

Funds:

Supported by National Natural Science Foundation of Youth Fund of China (60705021), Research Project for Application Foundation of Yunnan Province (2013FB026), Cultivation Program of Talents of Yunnan Province (KKSY201303048), and Focal Program for Education Office of Yunnan Province (2013Z130)

摘要

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.
- 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
Abstract: 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.
- Movement-related potentials (MRPs) /
- grip force movement mode /
- support vector machine (SVM) /
- brain-computer interface (BCI) /
- brain-machine interaction control (BMIC) /
- brain-controlled robot interface (BCRI)

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参考文献(27)

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基于运动相关皮层电位握力运动模式识别研究

doi: 10.3724/SP.J.1004.2014.01045

作者简介:
李洪谊中国科学院沈阳自动化研究所研究员. 主要研究方向为医疗机器人系统，机器人遥操作，微小机器人，机器人学与认知科学相结合的新型人机融合技术，非线性控制. E-mail：hli@sia.cn

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Recognition of Actual Grip Force Movement Modes Based on Movement-related Cortical Potentials

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基于运动相关皮层电位握力运动模式识别研究

doi: 10.3724/SP.J.1004.2014.01045

作者简介: 李洪谊 中国科学院沈阳自动化研究所研究员. 主要研究方向为医疗机器人系统，机器人遥操作，微小机器人，机器人学与认知科学相结合的新型人机融合技术，非线性控制. E-mail：hli@sia.cn

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Recognition of Actual Grip Force Movement Modes Based on Movement-related Cortical Potentials

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作者简介:
李洪谊中国科学院沈阳自动化研究所研究员. 主要研究方向为医疗机器人系统，机器人遥操作，微小机器人，机器人学与认知科学相结合的新型人机融合技术，非线性控制. E-mail：hli@sia.cn