Comparing parametric and nonparametric methods for detecting phase synchronization in EEG

S. M. Gordon, P. J. Franaszczuk, W. D. Hairston, M. Vindiola, K. McDowell

Research output: Contribution to journalArticlepeer-review

Abstract

Detecting significant periods of phase synchronization in EEG recordings is a non-trivial task that is made especially difficult when considering the effects of volume conduction and common sources. In addition, EEG signals are often confounded by non-neural signals, such as artifacts arising from muscle activity or external electrical devices. A variety of phase synchronization analysis methods have been developed with each offering a different approach for dealing with these confounds. We investigate the use of a parametric estimation of the time-frequency transform as a means of improving the detection capability for a range of phase analysis methods. We argue that such an approach offers numerous benefits over using standard nonparametric approaches. We then demonstrate the utility of our technique using both simulated and actual EEG data by showing that the derived phase synchronization estimates are more robust to noise and volume conduction effects.

Original languageEnglish (US)
Pages (from-to)247-258
Number of pages12
JournalJournal of Neuroscience Methods
Volume212
Issue number2
DOIs
StatePublished - Jan 30 2013
Externally publishedYes

Keywords

  • Autoregressive modeling
  • EEG
  • Phase synchronization

ASJC Scopus subject areas

  • Neuroscience(all)

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