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An extended multivariate autoregressive framework for EEG-based information flow analysis of a brain network
conference contribution
posted on 2013-01-01, 00:00 authored by Imali HettiarachchiImali Hettiarachchi, Shady MohamedShady Mohamed, Luke Nyhof, Saeid NahavandiRecently 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.
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IEEE Engineering in Medicine and Biology Society. Conference (35th : 2012 : Osaka, Japan)Pagination
3945 - 3948Publisher
IEEELocation
Osaka, JapanPlace of publication
Piscataway, N.J.Start date
2013-07-03End date
2013-07-07ISBN-13
9781457702143ISBN-10
1457702142Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2013, IEEETitle of proceedings
EMBC 2013 : Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology SocietyUsage metrics
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