[1]尚志刚,沈晓阳,李蒙蒙,等.基于格兰杰因果的效应性连接分析方法综述[J].郑州大学学报(工学版),2020,41(03):1-7.[doi:10.13705/j.issn.1671-6833.2020.02.014]
　Shang Zhigang Shen Xiaoyang Li Mengmeng Wanhong.A Review of Granger Causality-Based Effect Linkage Analysis Methods[J].Journal of Zhengzhou University (Engineering Science),2020,41(03):1-7.[doi:10.13705/j.issn.1671-6833.2020.02.014]
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基于格兰杰因果的效应性连接分析方法综述()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
41
期数:
2020年03期
页码:
1-7
栏目:
出版日期:
2020-07-29

文章信息/Info

Title:
A Review of Granger Causality-Based Effect Linkage Analysis Methods
作者:
尚志刚1; 2; 沈晓阳1; 2; 李蒙蒙1; 2; 万红1; 2
1. 郑州大学电气工程学院;2. 郑州大学河南省脑科学与脑机接口技术重点实验室
Author(s):
Shang Zhigang 1; 2Shen Xiaoyang 1; 2Li Mengmeng 1; 2Wanhong 1; 2
1. School of Electrical Engineering, Zhengzhou University; 2. Henan Provincial Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University
关键词:
格兰杰因果; 效应性连接; 神经信号; 信息流
Keywords:
Granger causality; effectivity connection; neural signals; Information Flow
DOI:
10.13705/j.issn.1671-6833.2020.02.014
文献标志码:
A
摘要:
效应性连接分析是研究大脑不同脑区之间交互作用的重要手段，目前基于格兰杰因果关系的效应性连接分析方法在多脑区神经信号分析中得到了国内外学者的广泛使用.首先对此类方法中常用代表性算法的计算原理与功能特点进行系统地介绍，并结合仿真算例对这些算法的功能特性进行对比，然后对此类方法在实际应用中应注意的要点进行总结，最后以广义偏定向相干及其改进算法为例，展示了在实际脑电数据集上的应用效果.
Abstract:
Effective connectivity analysis is an important approach to study the interaction between different regions of the brain. At present, the effective connectivity analysis methods based on Granger causality have been widely used by scholars around the world in neural signals analysis of multi-brain regions. First of all, the calculation principle and functional characteristics of representative algorithms commonly used in this kind of method were systematically introduced. Simulation case was carried out to compare the characteristics of different algorithms, and then the key points that should be paid attention to in practical application of this kind of method were summarized. Finally, Generalized Partial Directed Coherence and its improved algorithm were taken as examples to show the application effect on the actual electroencephalogram data set.
更新日期/Last Update: 2020-07-28