Effect of linear mixing in EEG on synchronization and complex network measures studied using the Kuramoto model

Negar Ahmadi (Corresponding author), Yulong Pei, Mykola Pechenizkiy

    Research output: Contribution to journalArticleAcademicpeer-review

    4 Citations (Scopus)
    1 Downloads (Pure)


    Volume conduction in the brain may influence the synchronization between EEG signals considerably, as it may lead to detection of spurious functional couplings among the recording channels. It has been shown that the volume conduction effect can be approximated as a linear mixing of the electrical fields of the brain regions. In this paper, we investigate the reliability of various synchronization measures in the presence of the linear superposition in EEG time series. For this purpose, we applied linear mixing to artificially generated EEG times series using the Kuramoto model of coupled phase oscillators, which represents the behavior of coupled systems with local interactions at the fundamental level. Our simulation results showed that the phase-lag index and the synchronization measures based on the visibility graph algorithms were less sensitive to the linear mixing effect and could predict the coupling degree correctly even with strongly overlapping signals. The results of our further data analyses demonstrated the effect of linear superposition in time series on the behavior of various complex network measures. For each case, we provide recommendations for proper choices of synchronization measures to obtain complex network characteristics that are minimally sensitive to linear mixing.

    Original languageEnglish
    Pages (from-to)289-308
    Number of pages20
    JournalPhysica A: Statistical Mechanics and its Applications
    Publication statusPublished - 15 Apr 2019


    • EEG
    • Kuramoto model
    • Network measures
    • Synchronization
    • Volume conduction

    Fingerprint Dive into the research topics of 'Effect of linear mixing in EEG on synchronization and complex network measures studied using the Kuramoto model'. Together they form a unique fingerprint.

    Cite this