基于运动相关皮层电位握力运动模式识别研究
Recognition of Actual Grip Force Movement Modes Based on Movement-related Cortical Potentials
查看参考文献28篇
文摘
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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. |
其他语种文摘
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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. |
来源
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自动化学报
,2014,40(6):1045-1057 【核心库】
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DOI
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10.3724/sp.j.1004.2014.01045
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关键词
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运动相关电位
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握力运动模式
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支持向量机
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脑-机接口
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脑-机交互控制
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脑控机器人接口
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地址
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1.
昆明理工大学信息工程与自动化学院, 机器人学国家重点实验室, 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
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昆明理工大学信息工程与自动化学院, 昆明, 650500
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语种
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中文 |
文献类型
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研究性论文 |
ISSN
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0254-4156 |
学科
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自动化技术、计算机技术 |
基金
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国家自然科学基金青年基金
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云南省应用基础研究计划项目
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云南省级人培项目
;
云南省教育厅重点项目
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文献收藏号
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CSCD:5164001
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参考文献 共
28
共2页
|
1.
Scott S H. Converting thoughts into action.
Nature,2006,442(7099):141-142
|
被引
5
次
|
|
|
|
2.
Wolpaw J R. Brain-computer interface technology: a review of the first international meeting.
IEEE Transactions on Rehabilitation Engineering,2000,8(2):164-173
|
被引
142
次
|
|
|
|
3.
王行愚. 脑控:基于脑-机接口的人机融合控制.
自动化学报,2013,39(3):208-221
|
被引
50
次
|
|
|
|
4.
Gao S K.
Grand challenges in EEG based brain-computer interface,2013
|
被引
1
次
|
|
|
|
5.
伏云发. 直接脑控机器人接口技术.
自动化学报,2012,38(8):1229-1246
|
被引
12
次
|
|
|
|
6.
Decety J. The neurophysiological basis of motor imagery.
Behavioural Brain Research,1996,77(1/2):45-52
|
被引
10
次
|
|
|
|
7.
Pfurtscheller G. Motor imagery and direct braincomputer communication.
Proceedings of the IEEE,2001,89(7):1123-1134
|
被引
50
次
|
|
|
|
8.
Cunnington R. Movement-related potentials associated with movement preparation and motor imagery.
Experimental Brain Research,1996,111(3):429-436
|
被引
1
次
|
|
|
|
9.
Pfurtscheller G. Event-related EEG/MEG synchronization and desynchronization: basic principles.
Clinical Neurophysiology,1999,110(11):1842-1857
|
被引
121
次
|
|
|
|
10.
Neuper C. ERD/ERS patterns reflecting sensorimotor activation and deactivation.
Progress in Brain Research,2006,159:211-222
|
被引
16
次
|
|
|
|
11.
Shibasaki H. What is the bereitschaftspotential?.
Clinical Neurophysiology,2006,117(11):2341-2356
|
被引
12
次
|
|
|
|
12.
do Nascimento O F. Movement related parameters modulate cortical activity during imaginary isometric plantar-flexions.
Experimental Brain Research,2006,171(1):78-90
|
被引
9
次
|
|
|
|
13.
Pfurtscheller G. EEG-based discrimination between imagination of right and left hand movement.
Electroencephalography and Clinical Neurophysiology,1997,103(6):642-651
|
被引
18
次
|
|
|
|
14.
Neuper C. Enhancement of left-right sensorimotor EEG differences during feedbackregulated motor imagery.
Journal of Clinical Neurophysiology,1999,16(4):373-382
|
被引
5
次
|
|
|
|
15.
SchlAosgl A. Characterization of four class motor imagery EEG data for the BCI-competition 2005.
Journal of Neural Engineering,2005,2(4):L14-L22
|
被引
20
次
|
|
|
|
16.
Gu Y. Single-trial discrimination of type and speed of wrist movements from EEG recordings.
Clinical Neurophysiology,2009,120(8):1596-1600
|
被引
5
次
|
|
|
|
17.
Gu Y. O2ine identification of imagined speed of wrist movements in paralyzed ALS patients from single-trial EEG.
Frontiers in Neuroscience,2009,3:62
|
被引
1
次
|
|
|
|
18.
Gu Y. Identification of task parameters from movement-related cortical potentials.
Medical Biological Engineering Computer,2009,47(12):1257-1264
|
被引
3
次
|
|
|
|
19.
do Nascimento O F. Movement-related cortical potentials allow discrimination of rate of torque development in imaginary isometric plantar flexion.
IEEE Transactions on Biomedical Engineering,2008,55(11):2675-2678
|
被引
3
次
|
|
|
|
20.
Farina D. Optimization of wavelets for classification of movementrelated cortical potentials generated by variation of forcerelated parameters.
Journal of Neuroscience Methods,2007,162(1/2):357-363
|
被引
4
次
|
|
|
|
|