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Author Omidvarnia, A.H.en_US
Author Mesbah, M.en_US
Author Khlif, M.S.en_US
Author O'Toole, J.M.en_US
Author Colditz, P.en_US
Author Boashash, B.en_US
Available date 2012-06-18T08:28:35Zen_US
Publication Date 2011en_US
Publication Name Proc. of IEEE 2011 Annual International Conference of Engineering in Medicine and Biology Society,EMBC
Citation A. H. Omidvarnia, M. Mesbah, M. S. Khlif, J. M. O'Toole, P. B. Colditz, and B. Boashash, "Kalman filter-based time-varying cortical connectivity analysis of newborn EEG," in Proc. of IEEE 2011 Annual International Conference of Engineering in Medicine and Biology Society,EMBC, 2011, pp. 1423-1426en_US
Description This paper presents a time-varying cortical connectivity analysis for to a source localization approach within the inner layers of the newborn brain. (Additional details can be found in the comprehensive book on Time-Frequency Signal Analysis and Processing (see In addition, the most recent upgrade of the original software package that calculates Time-Frequency Distributions and Instantaneous Frequency estimators can be downloaded from the web site: This was the first software developed in the field, and it was first released publicly in 1987 at the 1st ISSPA conference held in Brisbane, Australia, and then continuously updated).en_US
Abstract Multivariate Granger causality in the timefrequency domain as a representation of time-varying cortical connectivity in the brain has been investigated for the adult case. This is, however, not the case in newborns as the nature of the transient changes in the newborn EEG is different from that of adults. This paper aims to evaluate the performance of the time-varying versions of the two popular Granger causality measures, namely Partial Directed Coherence (PDC) and direct Directed Transfer Function (dDTF). The parameters of the time-varying AR, that models the inter-channel interactions, are estimated using Dual Extended Kalman Filter (DEKF) as it accounts for both non-stationarity and non-linearity behaviors of the EEG. Using simulated data, we show that fast changing cortical connectivity between channels can be measured more accurately using the time-varying PDC. The performance of the time-varying PDC is also tested on a neonatal EEG exhibiting seizure.en_US
Language enen_US
Publisher IEEEen_US
Subject DEKFen_US
Subject EEG nonlinearity behavioren_US
Subject EEG nonstationarity behavioren_US
Subject Granger causality measuresen_US
Subject dDTFen_US
Subject directed transfer functionen_US
Subject dual extended Kalman filteren_US
Subject fast changing cortical connectivityen_US
Subject interchannel interactionsen_US
Subject neonatal EEG exhibiting seizureen_US
Subject newborn EEGen_US
Subject partial directed coherenceen_US
Subject time varying AR parametersen_US
Subject time varying PDCen_US
Subject time varying cortical connectivity analysisen_US
Subject time-frequency domain multivariate Granger causalityen_US
Title Kalman filter-based time-varying cortical connectivity analysis of newborn EEGen_US
Type Conference Paperen_US

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