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

Content Provider	IEEE Xplore Digital Library
Author	Hettiarachchi, I.T. Mohamed, S. Nyhof, L. Nahavandi, S.
Copyright Year	2013
Description	Author affiliation: Centre for Intell. Syst. Res., Deakin Univ., Geelong, VIC, Australia (Hettiarachchi, I.T.; Mohamed, S.; Nyhof, L.; Nahavandi, S.)
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.
Starting Page	3945
Ending Page	3948
File Size	273723
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6610408
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Brain models Analytical models Adaptation models Biological system modeling Electroencephalography Kalman filters
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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