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

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

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dc.contributor.advisor
dc.contributor.author Omidvarnia, A
dc.contributor.author Mesbah, M
dc.contributor.author O'Toole, J.M
dc.contributor.author Colditz, P
dc.contributor.author Boashash, B
dc.date.accessioned 2011-09-17T09:37:45Z
dc.date.available 2011-09-17T09:37:45Z
dc.date.issued 2011-05
dc.identifier.citation Systems, Signal Processing and their Applications (WOSSPA), 2011 7th International Workshop on Issue Date : 9-11 May 2011 On page(s): 179 - 182 en_US
dc.identifier.isbn 978-1-4577-0689-9
dc.identifier.uri http://hdl.handle.net/10576/10737
dc.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
dc.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
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject Adaptation model en_US
dc.subject Brain modeling en_US
dc.subject Electroencephalography en_US
dc.subject Kalman filters en_US
dc.subject Pediatrics en_US
dc.subject Time frequency analysis en_US
dc.subject autoregressive processes en_US
dc.subject electroencephalography en_US
dc.subject medical signal processing en_US
dc.subject neurophysiology en_US
dc.subject time-frequency analysis en_US
dc.subject adaptive AR modeling en_US
dc.subject cortical brain region en_US
dc.subject cortical neural recording en_US
dc.subject directed transfer function en_US
dc.subject high resolution time-frequency coherence function en_US
dc.subject multichannel newborn EEG recording en_US
dc.subject multivariate autoregressive model en_US
dc.subject partial directed coherence en_US
dc.subject short-time based stationary approach en_US
dc.subject time-varying DTF en_US
dc.subject time-varying MVAR representation en_US
dc.subject time-varying PDC en_US
dc.subject time-varying cortical neural connectivity analysis en_US
dc.subject time-varying multivariate AR modeling en_US
dc.subject Brain connectivity en_US
dc.subject newborn EEG en_US
dc.title Analysis of the time-varying cortical neural connectivity in the newborn EEG: A time-frequency approach en_US
dc.type Article en_US

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