中国组织工程研究

中国组织工程研究 ›› 2010, Vol. 14 ›› Issue (43): 8061-8064.doi: 10.3969/j.issn.1673-8225.2010.43.021

• 骨与关节图像与影像 bone and joint imaging • 上一篇    下一篇

基于Mu/Beta节律想象运动脑电信号特征的提取

黄思娟，吴效明   

  1. 华南理工大学生物科学与工程学院，广东省广州市 510006
  • 出版日期:2010-10-22 发布日期:2010-10-22
  • 通讯作者: 吴效明，博士生导师，华南理工大学生物科学与工程学院，广东省广州市 510006 bmxmwus@scut.edu.cn
  • 作者简介:黄思娟★，女，1985年生，江西省高安市人，汉族，华南理工大学生物医学工程专业在读硕士，主要从事生物医学信号检测及处理方面的研究。 huangsijuan123@163.com
  • 基金资助:

    广东省科技计划项目(2009B030801004)，课题名称：面向社区家庭的医疗服务与检测仪器。

Feature extraction of electroencephalogram for imagery movement based on Mu/Beta rhythm

Huang Si-juan, Wu Xiao-ming　   

  1. School of Bioscience and Bioengineer, South China University of Technology, Guangzhou  510006, Guangdong Province, China　
  • Online:2010-10-22 Published:2010-10-22
  • Contact: Wu Xiao-ming, Doctoral supervisor, School of Bioscience and Bioengineer, South China University of Technology, Guangzhou 510006, Guangdong Province, China bmxmwus@scut.edu.cn
  • About author:Huang Si-juan★, Studying for master’s degree, School of Bioscience and Bioengineer, South China University of Technology, Guangzhou 510006, Guangdong Province, China huangsijuan123@163.com
  • Supported by:

     the Science and Technology Development Program of Guangdong Province, No. 2009B030801004*

PDF

1458

被引次数

25

摘要：

背景：不同的运动会产生不同的脑电信号，脑机接口技术就是利用脑电信号的特异性，通过现代信号处理技术和外部的连接实现人脑与外部设备的通信。以实现脑机接口在线研究的目标，首先要解决的是脑电信号处理的速度问题。
目的：研究快速、准确地提取脑电信号特征及分类的方法。
方法：充分利用想象运动过程中，脑电信号中Mu/Beta节律的事件相关同步化和去同步化特性，以2003年BCI竞赛数据为处理对象，采用带通滤波和小波包分析的方法提取Mu、Beta节律，提取C3、C4两通道上的能量平均值形成二维特征向量，利用matlab自带的classify函数进行分类。
结果与结论：通过对训练数据进行测试得到较为合适的参数，利用该参数对同等条件下的训练数据和测试数据分别进行判别，分类正确率分别达到87.857%和88.571%。

关键词: 特征提取与分类, 脑电信号, 事件相关同步化/去同步化, 想象运动, 小波包分析

Abstract:

BACKGROUND: Different sports produce different electroencephalogram (EEG) signals. Brain-computer interface (BCI) utilized characteristics of EEG to communicate brain and external device by modern signal processing technique and external connections. The speed of EEG signals processing is important for BCI online research.
OBJECTIVE: To investigate a rapid and accurate method for extracting and classifying EEG for imagery movement.
METHODS: Using the attribute of event-related synchronization and event-related desynchronization during imagery movement, the BCI dataset of 2003 was processed. Mu/Beta rhythm was obtained from bandpass filtering and wavelet package analysis. Then feature was formed by the average energy of lead C3, C4, and was sorted out by the function classify of matlab.
RESULTS AND CONCLUSION: Appropriate parameters were obtained by detection of training data and used for identification of training data and testing data, with a correct rate of classification of 87.857% and 88.571%.

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