计算机应用 ›› 2018, Vol. 38 ›› Issue (3): 836-841.DOI: 10.11772/j.issn.1001-9081.2017082010

• 虚拟现实与多媒体计算 • 上一篇    下一篇

基于三维视觉指导的运动想象训练性能分析

胡敏, 李冲, 路荣荣, 黄宏程   

  1. 重庆邮电大学 通信与信息工程学院, 重庆 400065
  • 收稿日期:2017-08-17 修回日期:2017-09-21 出版日期:2018-03-10 发布日期:2018-03-07
  • 通讯作者: 黄宏程
  • 作者简介:胡敏(1971-),女,重庆人,副教授,硕士,CCF会员,主要研究方向:虚拟现实、脑机接口、通信网体系与协议;李冲(1992-),男,安徽阜阳人,硕士研究生,主要研究方向:脑机接口、虚拟现实交互;路荣荣(1995-),女,陕西宝鸡人,主要研究方向:图像处理;黄宏程(1979-),男,河南南阳人,副教授,博士,CCF会员,主要研究方向:模式识别、数据融合通信。
  • 基金资助:
    重庆市科委基础与前沿研究计划项目(cstc2014jcyjA40039);国家级大学生创新计划项目(教育部教高司[2016]45号)。

Performance analysis of motor imagery training based on 3D visual guidance

HU Min, LI Chong, LU Rongrong, HUANG Hongcheng   

  1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • Received:2017-08-17 Revised:2017-09-21 Online:2018-03-10 Published:2018-03-07
  • Supported by:
    This work is partially supported by the Basic and Frontier Research Project of Chongqing Science & Technology Commission (cstc2014jcyjA40039), the National Innovation Program for College Students (No. 45, Education Department, Ministry of Education).

摘要: 为提高视觉指导下运动想象(MI)的训练效率和脑机接口(BCI)的分类准确率,研究了虚拟现实(VR)环境对MI训练的影响以及不同视觉指导下脑电(EEG)分类模型的差异。首先,设计了三种三维手部交互动画及其EEG采集程序;然后,分别在头戴式头盔(HMD)和平面液晶屏(LCD)的呈现环境下,对5名健康被试进行了标准(单次实验5min)和长测(单次实验15min)两种实验方案的左右手MI训练;最后,通过对EEG数据的模式分类,分析了呈现环境和内容形式对分类准确率的影响。实验结果表明,在视觉指导的MI训练中,HMD与LCD的呈现方式存在显著性差异。HMD所呈现的VR环境能够提高MI分类准确率,延长单次训练时长;此外,不同视觉指导内容下的分类模型存在较大差别,当测试样本与训练样本为同一视觉指导内容时,其平均分类准确率较之不同情况高出16.34%。

关键词: 脑机接口, 运动想象, 视觉指导, 虚拟现实, 脑电

Abstract: To improve the training efficiency of Motor Imagery (MI) under visual guidance and the classification accuracy of Brain-Computer Interface (BCI), the influence of Virtual Reality (VR) environment on MI training and the differences of ElectroEncephaloGram (EEG) classification models under different visual guidance were studied. Firstly, three kinds of 3D hand interactive animation and EEG acquisition program were designed. Secondly, in the rendering environment of Helmet-Mounted Display (HMD) and planar Liquid Crystal Display (LCD), the left hand and right hand MI training was conducted on 5 healthy subjects, including standard experiment (the single experiment lasted for 5min) and long-time experiment (the single experiment lasted for 15min). Finally, through the pattern classification of EEG data, the influence of rendering environment and content form on classification accuracy was analyzed. The experimental results show that there is a significant difference in the presentation of HMD and LCD in visual guided MI training. The VR environment presented by HMD can improve the accuracy of MI classification and prolong the duration of single training. In addition, the classification model under different visual guidance content is also different. When the testing samples and training samples have the same visual guidance content, the average classification accuracy is 16.34% higher than that of different samples.

Key words: Brain-Computer Interface (BCI), Motor Imagery (MI), visual guidance, Virtual Reality (VR), ElectroEncephaloGram (EEG)

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