南京大学学报(自然科学版) ›› 2020, Vol. 56 ›› Issue (2): 159–166.doi: 10.13232/j.cnki.jnju.2020.02.001

• •    下一篇

基于EEG握力变化及想象单次识别研究

陈睿,伏云发()   

  1. 昆明理工大学信息工程与自动化学院，昆明，650500
  • 收稿日期:2019-12-01 出版日期:2020-03-30 发布日期:2020-04-02
  • 通讯作者: 伏云发 E-mail:fyf@ynu.edu.cn
  • 基金资助:
    国家自然科学基金(81470084)

Single⁃trial recognition of actual/imagined forces of hand clenching based on Electroencephalogram

Rui Chen,Yunfa Fu()   

  1. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming，650500, China
  • Received:2019-12-01 Online:2020-03-30 Published:2020-04-02
  • Contact: Yunfa Fu E-mail:fyf@ynu.edu.cn

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摘要：

目前基于运动想象(Motor Imagery,MI)的脑?机接口(Brain?Computer Interface,BCI)可提供的指令数相对较少，为增加新的控制参数，基于脑电(Electroencephalogram,EEG)研究握力变化及想象的单次识别.招募20名被试者参与实验，要求被试者用右手执行三种不同握力大小(4 kg，10 kg，16 kg)的实际或想象任务，对任务期间覆盖运动区的九个通道的EEG数据进行分析，采用共同空间模式(Common Spatial Pattern,CSP)提取特征，然后利用极限学习机(Extreme Learning Machine,ELM)和支持向量机(Support Vector Machine,SVM)进行单次识别.ELM对三类握力变化及想象的平均单次识别准确率分别为82.3%±2.1%和80%±1%，SVM对三类握力变化及其想象的平均单次识别准确率分别为86.3%±5.5%和83.7%±3.8%.实验结果表明，ELM和SVM能有效地识别三种不同握力大小的实际或想象任务，而SVM的分类结果更好，可望为MI?BCI增加新的控制参数提供新思路.

关键词: 脑?机接口, 脑 电, 握力变化想象, 极限学习机, 单次识别

Abstract:

At present,Brain?Computer Interface (BCI) based on Motor Imagery (MI) provides relatively few instructions. To add new control parameters,this paper studies the single?trial recognition of actual and imagined forces of hand clenching based on Electroencephalogram (EEG). Twenty subjects recruited to participate in the experiment. They were instructed to perform three different actual/imagined hand?clenching force tasks (4 kg,10 kg,16 kg) with their right hand. The EEG data of nine channels over the primary motor area and the supplementary motor area during the task were analyzed,and the Common Spatial Pattern (CSP) was used to extract features of actual/imagined force of hand clenching,which were single?trial recognized by two classifiers: Extreme Learning Machines (ELM) and Support Vector Machines (SVM). The average ELM single?trial recognition accuracy for three different actual/imagined hand?clenching force tasks was 82.3%±2.1% and 80%±1%,respectively. The average SVM single?trial recognition accuracy for three different actual/imagined hand?clenching force tasks was 86.3%±5.5% and 83.7%±3.8%,respectively. The results show that the ELM and SVM can effectively identify three different actual/imagined hand?clenching force tasks,and the classification result of SVM is better. This study is expected to provide a new idea for adding new control parameters to MI?BCI.

Key words: Brain?Computer Interface (BCI), Electroencephalogram (EEG), actual/imagined force change of hand clenching, Extreme Learning Machines (ELM), single?trial recognition

中图分类号: 

  • TP391.41

图1

不同等级握力、肌电曲线示意图"

图2

单个Trial执行握力或想象握力任务的时序图"

图3

实验设置及信号同步方法示意图"

图4

EEG数据采集电极位置排布图"

图5

4 kg，10 kg和16 kg实际和想象的右手握力地形图"

表1

ELM和SVM算法单次识别实际和想象右手握力变化的准确率"

算法ELMSVM
实际握力82.3%±2.1%86.3%±5.5%
想象握力80%±1%83.7%±3.8%
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