[1]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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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
41
Number of periods:
2020 03
Page number:
1-7
Column:
Public date:
2020-07-29
- Title:
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A Review of Granger Causality-Based Effect Linkage Analysis Methods
- Author(s):
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Shang Zhigang 1; 2Shen Xiaoyang 1; 2Li Mengmeng 1; 2Wanhong 1; 2
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1. School of Electrical Engineering, Zhengzhou University; 2. Henan Provincial Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University
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- Keywords:
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Granger causality; effectivity connection; neural signals; Information Flow
- CLC:
-
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- DOI:
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10.13705/j.issn.1671-6833.2020.02.014
- Abstract:
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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.