中国组织工程研究

中国组织工程研究 ›› 2010, Vol. 14 ›› Issue (39): 7331-7335.doi: 10.3969/j.issn.1673-8225.2010.39.027

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

基于功率谱峰值及时变线性分类算法的运动意识分类

任亚莉   

  1. 陇东学院物理与电子工程学院，甘肃省庆阳市 745000
  • 出版日期:2010-09-24 发布日期:2010-09-24
  • 作者简介:任亚莉★，女，1970年生，汉族，甘肃省庆阳市人，2007年兰州理工大学毕业，硕士，副教授，主要从事生物医学信号检测分析方面的研究。 renyali888@sohu.com
  • 基金资助:

    甘肃省高等学校研究生导师科研项目计划资助(0710-05)，课题名称：基于MATLAB的信号与系统仿真研究。

Classification of imaginary hand movements based on kurtorsis of power spectral and time-variable linear classifier

Ren Ya-li   

  1. Physics and Electronic Engineering College, Longdong University, Qingyang  745000, Gansu Province, China
  • Online:2010-09-24 Published:2010-09-24
  • About author:Ren Ya-li★, Master, Associate professor, Physics and Electronic Engineering College, Longdong University, Qingyang 745000, Gansu Province, China renyali888@sohu.com
  • Supported by:

    the Scientific Research Program Foundation of Postgraduate Supervisor, Gansu Provincial High Institutes, No. 0710-05*

PDF

763

被引次数

12

摘要：

背景：脑电信号的特征提取是脑机接口系统中一个重要的环节，如何快速有效地提取反映大脑意识任务状态的脑电特征是进行分类、正确解读意识任务的关键。目前，提取脑电信号特征通常采用功率谱密度估计、自回归模型和小波变换等方法，这些特征都是以脑电信号的线性化为前提，上述方法不能很好地反映出大脑的非线性动力学性质。
目的：分析脑电信号功率谱峰值在识别左右手想象运动中的作用。
方法：采用脑机接口2003竞赛中Graz科技大学提供的脑电数据，用小波包分解获取8~24 Hz脑电信号，计算C3，C4电极脑电信号的功率谱峰值作为脑电特征向量，运用时变线性分类算法对运动意识任务运行分类。
结果与结论：对140次实验的测试样本进行数据分析，最大分类正确率可达89.29%，最大互信息和信噪比分别为0.626 9 bit和1.384 8。C3，C4电极8~24 Hz脑电信号功率谱峰值能很好地反映左右手运动想象脑电特征的变化，与事件相关去同     步/事件相关同步现象变化一致，可在线识别左右手想象运动。

关键词: 脑电信号, 脑机接口, 特征提取, 功率谱峰值, 时变线性分类

Abstract:

BACKGROUND: Feature extraction of electroencephalogram (EEG) signals is an important step in the brain-computer interfaces (BCI) system. Effective and rapid EEG signals feature extraction is important for classification and correct understanding. Currently, power spectrum density estimation, autoregression model and wavelet transform have been used to extract EEG signals features. However, they cannot well reflect nonlinear dynamics of the brain.
OBJECTIVE: To explore the effect of kurtosis of power spectral (KPS) in the recognition of hand imagery.
METHODS: The data gained from BCI competition in 2003 provided by Graz University of Technology. The EEG signals ranging from 8 to 24 Hz were decomposed by wavelet packet. The KPS of C3 and C4 were calculated respectively. The KPS was defined as the feature vector. The left and right hand motor imaginary tasks were distinguished by the time-variable linear classifier.
RESULTS AND CONCLUSION: The proposed method was applied to the test data set with 140 trails. The satisfactory results were obtained with the highest classification accuracy of 89.29%. The maximum mutual information was 0.6269 bit. The Signal-to-Noise Ratio was 1.3848. The KPS on channels C3 and C4 between 8 and 24Hz was coincident with event-related desynchronization and event-related synchronization. The method is simple and quick and it is a promising method for on-line BCI system.

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