Related Experiment Video
Updated: Oct 17, 2026
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
Published on: August 7, 2017
[Research on effective connectivity of intracerebral electroencephalogram based on Wiener-Granger Causality Index
Chunfeng Yang1, Wentao Xiang2, Jiasong Wu3
1Key Laboratory of Computer Network and Information Integration of Ministry of Education, School of Computer Science and Engineering, Southeast University, Nanjing 210096, P.R.China;International Joint Research Laboratory of Information Display and Visualization, Southeast University, Nanjing 210096, P.R.China;Centre de Recherche en Information Biomédicale Sino-français, Nanjing 210096 & Rennes 35000, P.R.China & France.chunfeng.yang@seu.edu.cn.
Abstract:
The objective is to deal with brain effective connectivity among epilepsy electroencephalogram (EEG) signals recorded by use of depth electrodes in the cerebral cortex of patients suffering from refractory epilepsy during their epileptic seizures. The Wiener-Granger Causality Index (WGCI) is a well-known effective measure that can be useful to detect causal relations of interdependence in these kinds of EEG signals. It is based on the linear autoregressive model, and the issue of the estimation of the model parameters plays an important role in the calculation accuracy and robustness of WGCI to do research on brain effective connectivity. Focusing on this issue, a modified Akaike's information criterion algorithm is introduced in the computation of the WGCI to estimate the orders involved in the underlying models and in order to advance the performance of WGCI to detect brain effective connectivity. Experimental results support the interesting performance of the proposed algorithm to characterize the information flow both in a linear stochastic system and a physiology-based model.
