Analysis of the time-varying cortical neural connectivity in the newborn EEG: A time-frequency approach

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contributor.author Omidvarnia, A en_US
contributor.author Mesbah, M en_US
contributor.author O'Toole, J.M. en_US
contributor.author Colditz, P en_US
contributor.author Boashash, B en_US
date.accessioned 2011-09-17T09:37:45Z en_US
date.available 2011-09-17T09:37:45Z en_US
date.issued 2011-05 en_US
identifier.citation Omidvarnia, A.; Mesbah, M.; O'Toole, J.M.; Colditz, P.; Boashash, B., "Analysis of the time-varying cortical neural connectivity in the newborn EEG: A time-frequency approach," Systems, Signal Processing and their Applications (WOSSPA), 2011 7th International Workshop on , vol., no., pp.179,182, 9-11 May 2011 en_US
identifier.isbn 978-1-4577-0689-9 en_US
identifier.uri http://hdl.handle.net/10576/10737 en_US
identifier.uri http://dx.doi.org/10.1109/WOSSPA.2011.5931445
description This paper aims to assess and compare the performance of two brain connectivity measures based on time-varying multivariate AR modelling for newborn EEG analysis. en_US
description.abstract Relationships between cortical neural recordings as a representation of functional connectivity between cortical brain regions were quantified using different time-frequency criteria. Among these, Partial Directed Coherence (PDC) and Directed Transfer Function (DTF) and their extensions have found wide acceptance. This paper aims to assess and compare the performance of these two connectivity measures that are based on time-varying multivariate AR modeling. The time-varying parameters of the AR model are estimated using an Adaptive AR modeling (AAR) approach and a short-time based stationary approach. The performance of these two approaches is compared using both simulated signal and a multichannel newborn EEG recording. The results show that the time-varying PDC outperforms the time-varying DTF measure. The results also point to the limitation of the AAR algorithm in tracking rapid parameter changes and the drawback of the short-time approach in providing high resolution time-frequency coherence functions. However, it can be demonstrated that time-varying MVAR representations of the cortical connectivity will potentially lead to better understanding of non-symmetric relations between EEG channels. en_US
language.iso en en_US
publisher IEEE en_US
subject Adaptation model en_US
subject Brain modeling en_US
subject Electroencephalography en_US
subject Kalman filters en_US
subject Pediatrics en_US
subject Time frequency analysis en_US
subject autoregressive processes en_US
subject electroencephalography en_US
subject medical signal processing en_US
subject neurophysiology en_US
subject time-frequency analysis en_US
subject adaptive AR modeling en_US
subject cortical brain region en_US
subject cortical neural recording en_US
subject directed transfer function en_US
subject high resolution time-frequency coherence function en_US
subject multichannel newborn EEG recording en_US
subject multivariate autoregressive model en_US
subject partial directed coherence en_US
subject short-time based stationary approach en_US
subject time-varying DTF en_US
subject time-varying MVAR representation en_US
subject time-varying PDC en_US
subject time-varying cortical neural connectivity analysis en_US
subject time-varying multivariate AR modeling en_US
subject Brain connectivity en_US
subject newborn EEG en_US
title Analysis of the time-varying cortical neural connectivity in the newborn EEG: A time-frequency approach en_US
type Article en_US


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