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Cited by in F6Publishing
For: Zhang C, Cong F, Kujala T, Liu W, Liu J, Parviainen T, Ristaniemi T. Network Entropy for the Sequence Analysis of Functional Connectivity Graphs of the Brain. Entropy (Basel) 2018;20:E311. [PMID: 33265402 DOI: 10.3390/e20050311] [Cited by in Crossref: 7] [Cited by in F6Publishing: 2] [Article Influence: 1.8] [Reference Citation Analysis]
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1 Zhang C, Sun L, Ge S, Chang Y, Jin M, Xiao Y, Gao H, Wang L, Cong F. Quantitative evaluation of short-term resting-state brain networks for primary insomnia diagnosis. Biomedical Signal Processing and Control 2022;74:103498. [DOI: 10.1016/j.bspc.2022.103498] [Reference Citation Analysis]
2 Liu C, Ma Y, Zhao J, Nussinov R, Zhang Y, Cheng F, Zhang Z. Computational network biology: Data, models, and applications. Physics Reports 2020;846:1-66. [DOI: 10.1016/j.physrep.2019.12.004] [Cited by in Crossref: 32] [Cited by in F6Publishing: 6] [Article Influence: 16.0] [Reference Citation Analysis]
3 Saha P, Sarkar D. Allometric scaling of von Neumann entropy in animal connectomes and its evolutionary aspect. Physica A: Statistical Mechanics and its Applications 2022;600:127503. [DOI: 10.1016/j.physa.2022.127503] [Reference Citation Analysis]
4 Liu L, Ren J, Li Z, Yang C. A review of MEG dynamic brain network research. Proc Inst Mech Eng H 2022;236:763-74. [PMID: 35465768 DOI: 10.1177/09544119221092503] [Reference Citation Analysis]
5 Wang J, Wu X, Li M. Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer's Disease. Entropy (Basel) 2021;23:216. [PMID: 33579012 DOI: 10.3390/e23020216] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Liu X, Li G, Wang S, Wan F, Sun Y, Wang H, Bezerianos A, Li C, Sun Y. Toward practical driving fatigue detection using three frontal EEG channels: a proof-of-concept study. Physiol Meas 2021;42. [PMID: 33780920 DOI: 10.1088/1361-6579/abf336] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]