结合运动想象和动作观察的伸-握动作识别方法

王保增 1; 李大海 2; 张进华 1; 洪军 1; 仲文远 1

doi:10.7652/xjtuxb201910021

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结合运动想象和动作观察的伸-握动作识别方法

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- 结合运动想象和动作观察的伸-握动作识别方法
- A Recognition Method of Spread and Grasp Actions Combining Motion Imagination and Action Observation
- 西安交通大学学报 2019年53卷第10期页码：151-158
- 作者机构：
  
  西安交通大学现代设计与转子轴承系统教育部重点实验室,西安,710049
- 作者简介：
- 基金信息：
  
  : 2019-03-16。作者简介: 王保增(1984—),男,博士生;张进华(通信作者),男,副教授,博士生导师。基金项目: 十三五装备预研领域基金资助项目(61400030701)
- DOI：10.7652/xjtuxb201910021
  中图分类号： R318.04
- 网络首发：2019-10-10，
  
  纸质出版：2019
- 稿件说明：
移动端阅览
王保增 1, 李大海 2, 张进华 1, 等. 结合运动想象和动作观察的伸-握动作识别方法[J]. 西安交通大学学报, 2019,53(10):151-158.

A Recognition Method of Spread and Grasp Actions Combining Motion Imagination and Action Observation[J]. 2019, 53(10): 151-158.
王保增 1, 李大海 2, 张进华 1, 等. 结合运动想象和动作观察的伸-握动作识别方法[J]. 西安交通大学学报, 2019,53(10):151-158. DOI： 10.7652/xjtuxb201910021.

A Recognition Method of Spread and Grasp Actions Combining Motion Imagination and Action Observation[J]. 2019, 53(10): 151-158. DOI： 10.7652/xjtuxb201910021.

摘要

为了获取单侧手部在伸-握动作下与运动想象直接相关的脑电信号(EEG)特征并实现精细动作有效识别

提出了结合运动想象和动作观察的单侧手部伸-握动作精细识别方法。首先

设计了结合运动想象和动作观察的复合范式

增强了伸-握动作所对应大脑皮层的激活程度

并探索了在运动想象、运动观察和复合范式下大脑皮层的激活规律; 然后利用小波变换重构各个通道EEG并计算相应多尺度熵

并通过共空间模式获取伸-握动作所对应EEG特征; 最后

利用线性判别式法从EEG特征中识别“伸开”和“握拳”精细动作。实验结果表明:相比单一实验范式的运动想象或动作观察

该方法在低频(5～20 Hz)时更能有效地激活运动区和枕区的神经元; 同时

“伸开”动作对应EEG的多尺度熵明显大于“握拳”动作的熵值

实现了单侧手部伸-握动作精细意图感知

其平均正确率可达85.7%。

Abstract

A recognition method for spread and grasp actions of a hand that combines motion imagination and action observation is proposed to obtain electroencephalogram(EEG)feature that is directly relevant to motor imagery of spread and grasp movements of the hand and to effectively identify fine actions.First

a compound paradigm that combines motor imagery and action observation(short for compound paradigm)is designed to enhance activation of the human cerebral cortex associated with the spread and grasp actions of a hand.Activation rules of the cerebral cortex are investigated under motor imagery

action observation and compound paradigm.Then

EEG is reconstructed for each channel by using a wavelet reconstruction and the multiscale entropy of EEG is calculated.EEG features of spread and grasp actions of the hand are extracted through a common spatial pattern.Finally

both the -spread- and -grasp- actions are identified from EEG features based on a linear discriminant method.Experimental

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references

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