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

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  • Ahmadi, Negar
  • Pei, Yulong
  • Pechenizkiy, Mykola

Abstract

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.

Suggested Citation

  • Ahmadi, Negar & Pei, Yulong & Pechenizkiy, Mykola, 2019. "Effect of linear mixing in EEG on synchronization and complex network measures studied using the Kuramoto model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 289-308.
    • Handle: RePEc:eee:phsmap:v:520:y:2019:i:c:p:289-308
    DOI: 10.1016/j.physa.2019.01.003
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    References listed on IDEAS

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    Cited by:

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    2. Cui, Qian & Li, Lulu & Cao, Jinde & Alsaadi, Fawaz E., 2022. "Synchronization of Kuramoto-oscillator networks under event-triggered delayed impulsive control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    3. Guo, Jia-Yi & Cai, Qing & An, Jian-Peng & Chen, Pei-Yin & Ma, Chao & Wan, Jun-He & Gao, Zhong-Ke, 2022. "A Transformer based neural network for emotion recognition and visualizations of crucial EEG channels," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).

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