中国组织工程研究

中国组织工程研究 ›› 2011, Vol. 15 ›› Issue (4): 657-659.doi: 10.3969/j.issn.1673-8225.2011.04.020

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

一种多维生理信号分析的新方法及其应用

崔  园，张军鹏   

  1. 成都医学院人文信息管理学院计算机教研室，四川省成都市    610083
  • 收稿日期:2010-08-24 修回日期:2010-10-19 出版日期:2011-01-22 发布日期:2011-01-22
  • 通讯作者: 张军鹏，硕士，副教授，成都医学院人文信息管理学院计算机教研室，四川省成都市 610083 junpeng.zhang@gmail.com
  • 作者简介:崔园★，女，1978年生，天津市人，汉族，2008年电子科技大学毕业，硕士，讲师，主要从事信号处理、数据挖掘方面的研究。 bubblecui@163.com

A new multiple dimensional approach for analyzing correlated brain activities

Cui Yuan, Zhang Jun-peng   

  1. Department of Computer Science, School of Humanities and Information, Chengdu Medical College, Chengdu  610083, Sichuan Province, China
  • Received:2010-08-24 Revised:2010-10-19 Online:2011-01-22 Published:2011-01-22
  • Contact: Zhang Jun-peng, Master, Associate professor, Department of Computer Science, School of Humanities and Information, Chengdu Medical College, Chengdu 610083, Sichuan Province, China junpeng.zhang@ gmail.com
  • About author:Cui Yuan★, Master, Lecturer, Department of Computer Science, School of Humanities and Information, Chengdu Medical College, Chengdu 610083, Sichuan Province, China bubblecui@163.com

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摘要：

背景：脑电源之间的高度相关性会导致脑电相关矩阵的秩缺损，经典的时空源定位方法不能定位相干源。
目的：发展一种相干源成像的方法，用于脑电源头表成像。
方法：提出一种多变量相关系数分解法。通过分解相关系数矩阵，得到对应变量的相关指标，该指标可以用来衡量该变量与整个系统的相关程度。通过数字试验，验证这种解法的正确性。
结果与结论：这种多变量相关系数矩阵分解算法，把变量两两之间的关系转换为每个变量与产生变量的系统之间的关系，便于相关程度的直观表达和理解，除了在认知神经科学方面有应用前景外，这种方法对其他领域的多变量分析也有一定的参考价值。

关键词: 脑电, 相关系数, 成像, 多维数据分析, 生理信号

Abstract:

BACKGROUND: High correlation between electroencephalogram (EEG) sources can cause rank deficit of the correlation matrix of EEG scalp recordings. Classical spatio-temporal EEG source localization cannot localize such sources.
OBJECTIVE: To develop a novel method to image correlated EEG sources.
METHODS: An algorithm, termed multivariate correlation coefficient matrix decompositions (MVMD), was proposed. Correlation index, obtained by decomposing cc matrix, was a measure of the degree of correlations between the specified channels and the system. The numerical experiment proved that this kind of matrix decomposition was correct and reasonable.
RESULTS AND CONCLUSION: MVMD transforms the relations between each pair of variables into those between each pair and the system generating all the variables. Such transform is useful in visualizing the relations between variables. It has promising prospect in cognitive neuroscience and other fields associated with multiple variable analysis.

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