An extended multivariate autoregressive framework for EEG-based information flow analysis of a brain network

Hettiarachchi, Imali T., Mohamed, Shady, Nyhof, Luke and Nahavandi, Saeid 2013, An extended multivariate autoregressive framework for EEG-based information flow analysis of a brain network, in EMBC 2013 : Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Piscataway, N.J., pp. 3945-3948.

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Title An extended multivariate autoregressive framework for EEG-based information flow analysis of a brain network
Author(s) Hettiarachchi, Imali T.
Mohamed, Shady
Nyhof, Luke
Nahavandi, Saeid
Conference name IEEE Engineering in Medicine and Biology Society. Conference (35th : 2012 : Osaka, Japan)
Conference location Osaka, Japan
Conference dates 3-7 Jul. 2013
Title of proceedings EMBC 2013 : Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Editor(s) [Unknown]
Publication date 2013
Conference series IEEE Engineering in Medicine and Biology Society
Start page 3945
End page 3948
Total pages 4
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Recently effective connectivity studies have gained significant attention among the neuroscience community as Electroencephalography (EEG) data with a high time resolution can give us a wider understanding of the information flow within the brain. Among other tools used in effective connectivity analysis Granger Causality (GC) has found a prominent place. The GC analysis, based on strictly causal multivariate autoregressive (MVAR) models does not account for the instantaneous interactions among the sources. If instantaneous interactions are present, GC based on strictly causal MVAR will lead to erroneous conclusions on the underlying information flow. Thus, the work presented in this paper applies an extended MVAR (eMVAR) model that accounts for the zero lag interactions. We propose a constrained adaptive Kalman filter (CAKF) approach for the eMVAR model identification and demonstrate that this approach performs better than the short time windowing-based adaptive estimation when applied to information flow analysis.
ISBN 9781457702167
9781457702143
Language eng
Field of Research 090399 Biomedical Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30057140

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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