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

Authors
Type
Published Article
Journal
Annual International Conference of the IEEE Engineering in Medicine and Biology Society
1557-170X
Publication Date
Volume
2013
Pages
3945–3948
Identifiers
DOI: 10.1109/EMBC.2013.6610408
PMID: 24110595
Source
Medline
License
Unknown

Abstract

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.

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