中国组织工程研究

中国组织工程研究 ›› 2011, Vol. 15 ›› Issue (48): 9007-9010.doi: 10.3969/j.issn.1673-8225.2011.48.019

• 数字化骨科 digital orthopedics • 上一篇    下一篇

基于多通道自适应自回归模型脑-机接口系统特征的提取

王  江1，2，徐桂芝1，王  磊1，张惠源2   

  1. 1河北工业大学电气工程学院，天津市  300130
    2唐山职业技术学院，河北省唐山市063000
  • 收稿日期:2011-05-30 修回日期:2011-07-18 出版日期:2011-11-26 发布日期:2011-11-26
  • 作者简介:王江★，男，1975年生，河北省唐山市人，汉族，2007年河北工业大学电工理论与新技术专业毕业，硕士，讲师，主要从事生物医学信号处理研究。 tswxwj@yeah.net
  • 基金资助:

    河北省自然科学基金(E2009000062)，具有被试针对性的运动想象脑机接口技术研究。

Features extraction of brain-computer interface based on adaptive autoregressive models

Wang Jiang1,2, Xu Gui-zhi1, Wang Lei1, Zhang Hui-yuan2   

  1. 1Electrical Engineering School of Hebei Technology University, Tianjin  300130, China
    2Tangshan Vocation and Technical College, Tangshan  063000, Hebei Province, China
  • Received:2011-05-30 Revised:2011-07-18 Online:2011-11-26 Published:2011-11-26
  • About author:Wang Jiang★, Master, Lecturer, Electrical Engineering School of Hebei Technology University, Tianjin 300130, China; Tangshan Vocation and Technical College, Tangshan 063000, Hebei Province, China tswxwj@yeah.net
  • Supported by:

    the Natural Science Foundation of Hebei Province, No. E2009000062*

PDF

925

被引次数

6

摘要：

背景：由于脑电图信号的非平稳特性，脑-机接口系统至今仍然没有走出实验室，制约脑-机接口实用的主要原因之一是由于被试生理或心理状态的干扰下，脑电特征信号动态变化，难以得到稳定可靠的分类特征。
目的：观察动态提取基于左手、右手和脚3种运动想象时的脑电信号分类特征，提高在线脑-机接口系统分类准确率和反应速度。
方法：共有3位自愿受试者参加了实验，按照屏幕上的提示分别想象左手、右手和脚3种运动，对采集到的脑电图信号，首先通过带通及拉普拉斯滤波，去除眼电等干扰；其次提取改进的多变量自适应自回归模型模型参数作为分类特征；最后与传统的自适应自回归模型和自回归模型方法进行了比较。
结果与结论：结果表明改进的多通道自适应自回归模型算法能够比较稳定的提取出对应左手、右手和脚的分类特征，有利于进一步改进在线脑-机接口数据分析算法的自适应能力，促进脑-机接口系统的实际应用。

关键词: 脑-机接口, 多通道自适应自回归模型, 支持向量机, 脑电信号, 数字化医学

Abstract:

BACKGROUND: Due to the non-stationary nature of electroencephalograms (EEG) signals, brain-computer interface (BCI) cannot walk out of the laboratory. Due to dynamical changes of the EEG signals under the disturbance of physical or psychological conditions, it is difficult to obtain stable classified features, which is one of the main reasons for the practical utility of BCI.
OBJECTIVE: To observe the classified features of EEG signals base on left hand, right hand and foot movement in order to improve the reaction rate and accuracy of classification in BCI
METHODS: Three subjects were selected for the BCI experiment. Three mental tasks with the imagination of left hand, right hand and foot movement were followed according to the screen instructions. The disturbance of electro-oculogram in EEG signal was removed by using band-pass and Laplace filter. Model parameter of many variables adaptive autoregressive models (MVAAR) was extracted as the classified features. The comparison between MVAAR and traditional adaptive autoregressive models was made.
RESULTS AND CONCLUSION: The result showed that classified features of left hand, right hand and foot movement could be extracted stably in MVAAR, which is beneficial to improve the adaptive ability of online BCI and the application of BCI system.

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