Measuring time-varying information flow in scalp EEG signals: orthogonalized partial directed coherence

Amir Omidvarnia, Ghasem Azemi, Boualem Boashash, John M. O'Toole, Paul B. Colditz, Sampsa Vanhatalo

Research output: Contribution to journalArticlepeer-review

71 Citations (Scopus)


This study aimed to develop a time–frequency method for measuring directional interactions over time and frequency from scalp-recorded electroencephalographic (EEG) signals in a way that is less affected by volume conduction and amplitude scaling. We modified the time-varying generalized partial directed coherence (tv-gPDC) method, by orthogonalization of the strictly causal multivariate autoregressive model coefficients, to minimize the effect of mutual sources. The novel measure, generalized orthogonalized PDC (gOPDC), was tested first using two simulated models with feature dimensions relevant to EEG activities. We then used the method for assessing event-related directional information flow from flash-evoked responses in neonatal EEG. For testing statistical significance of the findings, we followed a thresholding procedure driven by baseline periods in the same EEG activity. The results suggest that the gOPDC method 1) is able to remove common components akin to volume conduction effect in the scalp EEG, 2) handles the potential challenge with different amplitude scaling within multichannel signals, and 3) can detect directed information flow within a subsecond time scale in nonstationary multichannel EEG datasets. This method holds promise for estimating directed interactions between scalp EEG channels that are commonly affected by the confounding impact of mutual cortical sources.
Original languageEnglish
Pages (from-to)680-693
Number of pages14
JournalIEEE Transactions on Biomedical Engineering
Issue number3
Publication statusPublished - 2014
Externally publishedYes


  • Brain networks
  • connectivity analysis
  • directed coherence
  • EEG
  • multivariate autoregressive modeling
  • volume conduction


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