Research on effective connectivity of intracerebral electroencephalogram based on Wiener-Granger Causality Index modified by generalized Akaike's Information Criterion / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 665-671, 2018.
Article
in Chinese
| WPRIM
| ID: wpr-687578
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.
Full text:
Available
Index:
WPRIM (Western Pacific)
Type of study:
Etiology study
/
Prognostic study
Language:
Chinese
Journal:
Journal of Biomedical Engineering
Year:
2018
Type:
Article
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