NDLI: A New Strategy for Model Order Identification and Its Application to Transfer Entropy for EEG Signals Analysis

Content Provider	IEEE Xplore Digital Library
Author	Chunfeng Yang Le Bouquin Jeannes, R. Bellanger, J.-J. Huazhong Shu
Copyright Year	1964
Abstract	The background objective of this study is to analyze electrenocephalographic (EEG) signals recorded with depth electrodes during seizures in patients with drug-resistant epilepsy. Usually, different phases are observed during the seizure evolution, including a fast onset activity. We aim to ascertain how cerebral structures get involved during this phase, in particular whether some structures “drive” other ones. Regarding a recent theoretical information measure, namely the transfer entropy (TE), we propose two criteria, the first one is based on Akaike's information criterion, the second on the Bayesian information criterion, to derive models' orders that constitute crucial parameters in the TE estimation. A normalized index, named partial transfer entropy (PTE), allows for quantifying the contribution or the influence of a signal to the global information flow between a pair of signals. Experiments are first conducted on linear autoregressive models, then on a physiology-based model, and finally on real intracerebral EEG epileptic signals to detect and identify directions of causal interdependence. Results support the relevance of the new measures for characterizing the information flow propagation whatever unidirectional or bidirectional interactions.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	10
File Size	865797
Starting Page	1318
Ending Page	1327
File Format	PDF
ISSN	00189294
Volume Number	60
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Brain modeling Sociology Statistics Entropy Estimation Computational modeling Electroencephalography transfer entropy (TE) Bayesian information criterion (BIC) causality electrenocephalographic (EEG) signal physiology-based model
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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