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AuthorOmidvarnia, A.H.
AuthorMesbah, M.
AuthorKhlif, M.S.
AuthorO'Toole, J.M.
AuthorColditz, P.
AuthorBoashash, B.
Available date2012-06-18T08:28:35Z
Publication Date2011
Publication NameProc. of IEEE 2011 Annual International Conference of Engineering in Medicine and Biology Society,EMBC
CitationA. 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. 1423-1426
DescriptionThis 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).
AbstractMultivariate 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.
SubjectEEG nonlinearity behavior
SubjectEEG nonstationarity behavior
SubjectGranger causality measures
Subjectdirected transfer function
Subjectdual extended Kalman filter
Subjectfast changing cortical connectivity
Subjectinterchannel interactions
Subjectneonatal EEG exhibiting seizure
Subjectnewborn EEG
Subjectpartial directed coherence
Subjecttime varying AR parameters
Subjecttime varying PDC
Subjecttime varying cortical connectivity analysis
Subjecttime-frequency domain multivariate Granger causality
TitleKalman filter-based time-varying cortical connectivity analysis of newborn EEG
TypeConference Paper

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