Analysis of the time-varying cortical neural connectivity in the newborn EEG: A time-frequency approach
Systems, Signal Processing and their Applications(2011)
摘要
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.
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关键词
autoregressive processes,electroencephalography,medical signal processing,neurophysiology,time-frequency analysis,adaptive ar modeling,cortical brain region,cortical neural recording,directed transfer function,high resolution time-frequency coherence function,multichannel newborn eeg recording,multivariate autoregressive model,partial directed coherence,short-time based stationary approach,time-varying dtf,time-varying mvar representation,time-varying pdc,time-varying cortical neural connectivity analysis,time-varying multivariate ar modeling,kalman filters,time frequency analysis,kalman filter,pediatrics,time frequency
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