Effect of linear mixing in EEG on synchronization and complex network measures studied using the Kuramoto model
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DOI: 10.1016/j.physa.2019.01.003
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References listed on IDEAS
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Cited by:
- Peng, Xiaoyi & Zhao, Yi & Small, Michael, 2020. "Identification and prediction of bifurcation tipping points using complex networks based on quasi-isometric mapping," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
- 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).
- 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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Keywords
EEG; Volume conduction; Synchronization; Network measures; Kuramoto model;All these keywords.
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