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视觉诱发电位脑机接口关键技术研究 被引量:5

Researches of key techniques on brain-computer interface of visual evoked potential
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摘要 脑机接口(brain-computer interface,BCI)是近10年发展起来的一种新颖的人机接口方式.它是不依赖于脑的正常输出通路(外周神经系统及肌肉组织)的脑机(计算机或其它装置)通讯系统.脑机接口的一个重要用途不仅为那些思维正常但有严重运动障碍的患者提供语言交流和环境控制途径,还在工业、航空、军事等领域也有潜在的应用价值.本文介绍了基于视觉诱发电位脑机接口的工作原理,从系统设计、数据获取及处理方法等方面论述了脑机接口设计中的关键技术,最后指出了视觉诱发电位脑机接口存在的主要问题和发展趋势. Brain -computer interface (BCI) is a new kind of human computer interface being explored since last decade, and BCI is one kind of communication system which does not depend on the brain' s normal output channels of peripheral nerves and muscles. One of the most important functions is that this technology could be a new valuable augmentative communication option for those with severe motor disabilities that prevent them from using conventional augmentative technologies. BCI technology also has potential applications in other fields such as industry, space and defense. This paper introduces the working principle of visual evoked potential based BCI skill. Those key techniques included system design, methods of data acquisition and process. Finally, some main problems and future trends are also pointed out.
作者 王洪涛
出处 《重庆文理学院学报(自然科学版)》 2010年第1期69-74,共6页 Journal of Chongqing University of Arts and Sciences
基金 五邑大学青年基金项目(200906190203564)
关键词 脑机接口 视觉诱发电位 模式识别 特征提取 brain -computer interface visual evoked potential pattern cognition feature extraction
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  • 1王云华,杨福生.通过脑电偶极子进行视觉诱发响应的动态提取[J].中国生物医学工程学报,1994,13(3):191-199. 被引量:1
  • 2[1]Chang HT.Handbook of Physiology[J].1959,1(1):299-313.
  • 3[2]Kaveh M.A new method for the estimation of averaged evoked res ponse[J]. IEEE Trans. SMC, 1978,8(5):414-417.
  • 4[3]Woody CJ. Characterization of an adaptive filter for the analysis of variable latency meuroelectric signals[J]. Med Biol Eng,1967,5(6):539-553 .
  • 5[4]McGillem CD,Aunon, JI.Measurements of Signal Components in single visually evoked brain potentials. IEEE Trans. Biomed Eng,1977,24(2):232-241.
  • 6[5]McGillem CD. Evoked detection using continuous latency corr ected average[J].IEEE Trans.Biomed.Eng,1985, 32(3):371-379.
  • 7[6]De Weerd JPC.A poserior time-varying filtering of averaged evoked potentials.I.Introduction and conceptual basis[J].Biol,Cybernet,1981,41:211 -222.
  • 8[7]Widrow B, Stearns SD. Adaptive signal processing[M].New Jersey:Prenice-Hall, Inc, Englewood cliffs,1985.
  • 9[8]Hecox K, Tompkins WJ. Adaptive filtering of auditory evoked potent ials[C]. IEEE Eighth Annual International Conference of the EMBS.1986:402- 405.
  • 10[9]Thakor NV. Adaptive filtering of evoked potentials[J]. IEEE Trans. on Biomed. Eng.1987,34(1):6-11.

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  • 1徐宝国,宋爱国.基于小波变换和AR参数模型的脑电信号识别方法[J].数据采集与处理,2008,23(5):580-583. 被引量:6
  • 2RODER B, ROSLER F, HENNIGHAUSEN E, et al. Eventrelated Potentials during Auditory and Somatosensory Discrimination in Sighted and Blind Human Subjects[ J]. Cognitive Brain Research, 1996,4 ( 2 ) : 77 - 93.
  • 3SUTTON S, BRAREN M, ZUBIN J, et al. Information Delivery and the Sensory Evoked Potential [ J ]. Science, 1965, 155 : 1436 - 1439.
  • 4WOLPAW J, BIRBAUMER N, HEETDERKS W, et al. Brain-computer Interface Technology: A Review of the First International Meeting[ J]. IEEE Trans Rehabil Eng, 2000,8(2) :164 - 173.
  • 5EDLINGER Gunter, HOLZNER Clemens, GUGER Christoph, et al. Brain-Computer Interfaces for Goal Orientated Control of a Virtual Smart Home Environment [ C ]//Proceedings of the 4th International IEEE EMBS Conference on Neural Engineering Antalya,Turkey,2009:463 -465.
  • 6Piccione F, Giorgi F, Tonin P, et al. P300-based brain computer interface: Reliability and performance in healthy and par- alysed participants[J]. Clinical Neurophysiology, 2006, 117(3): 531-537.
  • 7Lebedev M A, Nicolelis M A. Brain-machine interfaces: Past, present and future[J ]. Trends in Neurosciences, 2006, 29 (9) : 536-546.
  • 8Vahid Abootalebi. A new approach for EEG feature extraction in P300-based lie detection[J ]. Computer Methods and Pro- grams in Biomedicine, 2008,10-14.
  • 9Lebedev MA, Nicolelis MA. Brain-machine interfaces: past, present and future[J]. Trends in Neurosciences, 2006, 29 (9) : 536-546.
  • 10Eduardo Bayro-eorrochano. The theory and use of the quatemion wavelet transform[J ]. Journal of Mathematical Imaging and Vision, 2006, 24: 19-35.

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