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

Omidvarnia, Amir, Azemi, Ghasem, Boashash, Boualem, O'Toole John M., Colditz, Paul and Vanhatalo, Sampsa (2013) Measuring time-varying information flow for scalp EEG signals: orthogonalized partial directed coherence. IEEE Transactions on Biomedical Engineering, Early Access 1-14. doi:10.1109/TBME.2013.2286394

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads

Author Omidvarnia, Amir
Azemi, Ghasem
Boashash, Boualem
O'Toole John M.
Colditz, Paul
Vanhatalo, Sampsa
Title Measuring time-varying information flow for scalp EEG signals: orthogonalized partial directed coherence
Journal name IEEE Transactions on Biomedical Engineering   Check publisher's open access policy
ISSN 0018-9294
1558-2531
Publication date 2013-10-18
Sub-type Article (original research)
DOI 10.1109/TBME.2013.2286394
Open Access Status
Volume Early Access
Start page 1
End page 14
Total pages 14
Place of publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Collection year 2014
Language eng
Formatted abstract
This study aimed to develop a time–frequency method that can measure 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 MVAR 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 directed information flow from flash-evoked EEG responses in neonatal EEG. For testing statistical significance of the findings, we used a significance-level threshold that was derived from a baseline period in the same EEG activity. The results suggest that the gOPDC method i) is able to remove common components akin to volume conduction effects in the scalp EEG, ii) handles the potential challenge with different amplitude scaling within multichannel signals and iii) can detect directed frequency-related information flow within a sub-second time scale in nonstationary multichannel EEG datasets. This method holds promise for estimating directed interactions between scalp-recorded EEG signals that are commonly challenged by the confounding volume conduction effect of mutual sources.
Keyword Brain networks
Connectivity analysis
Directed coherence
Electroencephalography
MVAR modeling
Volume conduction
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Date of Publication : 18 October 2013

Document type: Journal Article
Sub-type: Article (original research)
Collections: UQ Centre for Clinical Research Publications
Official 2014 Collection
School of Medicine Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 11 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 13 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Wed, 11 Dec 2013, 12:46:50 EST by Roheen Gill on behalf of UQ Centre for Clinical Research