期刊文献+

基于SSVEP的脑-机接口自动车系统研究 被引量:4

Research of brain-computer interface automatic vehicle system based on SSVEP
在线阅读 下载PDF
导出
摘要 阐述了视觉诱发电位用于脑-机接口的原理,系统采用单片机设计视觉刺激器,同时在LABVIEW平台上,利用希尔伯特黄变换实时提取诱发电位向量,产生脑机接口控制信号,并用于自动车控制系统,从而控制小车的前后左右运动。通过大量实验验证,设计的基于稳态视觉诱发电位的脑-机接口自动车控制系统,发送控制命令正确率高于83%,发送一个命令的平均时间低于5s,证明该系统的方案是可行的,具有较高的应用价值。 This paper mainly carried out proposes the research of SSVEP brain-computer interface automatic vehicle control systems,which describes the principles of the visual evoked potentials that used in brain-computer interface,and the single-chip is used to designs visual stimulation.Base on the LABVIEW platform,it also uses Hilbert Huang Transform to extract evoked potential vector continuously,which produces brain-computer interface control signals that can be applied to automatic vehicle control system to control the car around before and after exercise.According to a lot of experiments to verify,this sistem can send out the control commands that the correct rate is higher than 83% and can also send a command less than 5 seconds compared with the average time based on SSVEP,so it proves that the system is feasible and has a high application value.
出处 《电子测量技术》 2011年第12期70-72,共3页 Electronic Measurement Technology
关键词 稳态视觉诱发电位 脑-机接口 LABVIEW 自动车 steady-state visual evoked potential brain-computer interface LABVIEW automatic vehicle
  • 相关文献

参考文献10

二级参考文献103

共引文献192

同被引文献42

  • 1石维亮,王兴松,贾茜.基于Mecanum轮的全向移动机器人的研制[J].机械工程师,2007(9):18-21. 被引量:31
  • 2周鹏,曹红宝,熊屹,葛家怡,张爽,王明时.基于脑机接口的智能康复系统的设计[J].计算机工程与应用,2007,43(26):1-4. 被引量:19
  • 3宋治.对脑可塑性理论的思考[J].医学与哲学,1997,18(3):125-127. 被引量:2
  • 4GUGER C,HARKAM W,HERTNAES C,et al.Prosthetic control by an EEG-based brain-computer interface (BCI)[J].Assistive Technology on the Threshold of the New Millennium,1999 (6):590-595.
  • 5STASTNY J,SOVKA P,STANCAK A.EEG signal classification[C].Proceedings of the 23rd Annual International Conference of the IEEE on Engineering in Medicine and Biology Society,lstanbul,Turkey,2001,2020-2023.
  • 6STASTNY J,ZEJBRDLICH J,SOVKA P.Optimal parameterization selection for the brain-computer interface[C].AEE05 Proceedings of the 4th WSEAS international conference on Applications of electrical engineering,2005:300-304.
  • 7HAZRATI M K H,ERFANIAN A.An online EEG-based brain-computer interface for controlling hand grasp using an adaptive probabilistic neural network[J].Medical Engineering & Physics,2010,7 (32):730-739.
  • 8AHMADI M,ERFAN1AN A.An on-line BCI system for hand movement control using real-time recurrent probabilistic neural network[C].Neural Engineering,2009.NER'09.4th International IEEE/EMBS Conference,Antalya,2009:367-370.
  • 9STASTNY J,SOVKA P,STANCAK A.EEG signal classification:Introduction to the problem[J].Radio Engineering,2003,3(12):51-55.
  • 10RUCKAYL,ST ASTNY J,SOVKA P.Independent component analysis of movement-related EEG and classification of selected components[J].Applied Electronics,2006,6 (10):142-150.

引证文献4

二级引证文献36

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部