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Wiener Granger Causality Index based on a generalized Akaike's Information Criterion and its application in EEG
YANG Chunfeng 1 * #,XIANG Wentao 2,Wu Jiasong 3,LE BOUQUIN JEANNES Régine 1,SHU Huazhong 3
1.Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China
2. Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China
3. Centre de Recherche en Information Biomédicale sino-fran?ais (CRIBs)
*Correspondence author
#Submitted by
Subject:
Funding: Specialized Research Fund for the Doctoral Program of Higher Education (No.No. 20130092120035, No. 20120092120036, No. 20110092110023)
Opened online:28 August 2014
Accepted by: none
Citation: YANG Chunfeng,XIANG Wentao,Wu Jiasong.Wiener Granger Causality Index based on a generalized Akaike's Information Criterion and its application in EEG[OL]. [28 August 2014] http://en.paper.edu.cn/en_releasepaper/content/4607101
 
 
Our objective is to analyze EEG signals recorded with depth electrodes during seizures in patients with drug-resistant epilepsy. The Wiener-Granger Causality Index (WGCI) is an effective measure to detect causal relations of interdependence in EEG signals. In this paper, a generalized Akaike's Information Criterion (gAIC) algorithm is presented and incorporated in the WGCI to estimate the model orders which play an important role in such an approach. Experimental simulation results support the interesting performance of the proposed algorithm to characterize the information flow both in a linear stochastic system and in a physiology-based model.
Keywords:WGCI; gAIC; model order; iEEG
 
 
 

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