Research on effective connectivity of intracerebral electroencephalogram based on Wiener-Granger Causality Index modified by generalized Akaike's Information Criterion.
10.7507/1001-5515.201709032
- Author:
Chunfeng YANG
1
,
2
,
3
;
Wentao XIANG
4
,
5
;
Jiasong WU
1
,
2
,
5
;
Youyong KONG
1
,
5
;
Longyu JIANG
1
,
5
;
Jèannes Régine Le BOUQUIN
4
,
5
;
Huazhong SHU
1
,
2
,
5
Author Information
1. Key Laboratory of Computer Network and Information Integration of Ministry of Education, School of Computer Science and Engineering, Southeast University, Nanjing 210096, P.R.China
2. International Joint Research Laboratory of Information Display and Visualization, Southeast University, Nanjing 210096, P.R.China
3. Centre de Recherche en Information Biomédicale Sino-français, Nanjing 210096 & Rennes 35000, P.R.China & France.chunfeng.yang@seu.edu.cn.
4. INSERM U1099, LTSI, Université de Rennes 1, Rennes 35000, France
5. Centre de Recherche en Information Biomédicale Sino-français, Nanjing 210096 & Rennes 35000, P.R.China & France.
- Publication Type:Journal Article
- Keywords:
Akaike's information criterion;
brain connectivity;
causality index;
epilepsy;
physiology-based model
- From:
Journal of Biomedical Engineering
2018;35(5):665-671
- CountryChina
- Language:Chinese
-
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.
