{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T11:19:13Z","timestamp":1762341553697,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,10,30]],"date-time":"2020-10-30T00:00:00Z","timestamp":1604016000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61662045,61762045,61841201"],"award-info":[{"award-number":["61662045,61762045,61841201"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Functional brain network (FBN) is an intuitive expression of the dynamic neural activity interaction between different neurons, neuron clusters, or cerebral cortex regions. It can characterize the brain network topology and dynamic properties. The method of building an FBN to characterize the features of the brain network accurately and effectively is a challenging subject. Entropy can effectively describe the complexity, non-linearity, and uncertainty of electroencephalogram (EEG) signals. As a relatively new research direction, the research of the FBN construction method based on EEG data of fatigue driving has broad prospects. Therefore, it is of great significance to study the entropy-based FBN construction. We focus on selecting appropriate entropy features to characterize EEG signals and construct an FBN. On the real data set of fatigue driving, FBN models based on different entropies are constructed to identify the state of fatigue driving. Through analyzing network measurement indicators, the experiment shows that the FBN model based on fuzzy entropy can achieve excellent classification recognition rate and good classification stability. In addition, when compared with the other model based on the same data set, our model could obtain a higher accuracy and more stable classification results even if the length of the intercepted EEG signal is different.<\/jats:p>","DOI":"10.3390\/e22111234","type":"journal-article","created":{"date-parts":[[2020,10,30]],"date-time":"2020-10-30T09:29:32Z","timestamp":1604050172000},"page":"1234","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Construction and Application of Functional Brain Network Based on Entropy"],"prefix":"10.3390","volume":"22","author":[{"given":"Lingyun","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Computer, Nanchang University, Nanchang 330029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taorong","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Computer, Nanchang University, Nanchang 330029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqiang","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Computer, Nanchang University, Nanchang 330029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuli","family":"Zou","sequence":"additional","affiliation":[{"name":"Department of Computer, Nanchang University, Nanchang 330029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoming","family":"Bai","sequence":"additional","affiliation":[{"name":"Department of Computer, Nanchang University, Nanchang 330029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1360\/972009-2150","article-title":"Human brain connection group research: Brain structure network and brain function network","volume":"55","author":"Liang","year":"2010","journal-title":"Chin. Sci. Bull."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1089\/brain.2014.0330","article-title":"The Union of Shortest Path Trees of Functional Brain Networks","volume":"5","author":"Meier","year":"2015","journal-title":"Brain Connect."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"026023","DOI":"10.1088\/1741-2552\/aaaa76","article-title":"Reduced integration and improved segregation of functional brain networks in alzheimer\u2019s disease","volume":"15","author":"Kabbara","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102129","DOI":"10.1016\/j.bspc.2020.102129","article-title":"The functional brain network based on the combination of shortest path tree and its application in fatigue driving state recognition and analysis of the neural mechanism of fatigue driving","volume":"62","author":"Zou","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_5","unstructured":"Conrin, S.D., Zhan, L., Morrissey, Z.D., Xing, M., Forbes, A., Maki, P.M., Milad, M.R., Ajilore, O., and Leow, A. (2018). Sex-by-age differences in the resting-state brain connectivity. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1109\/JBHI.2016.2544061","article-title":"The Reorganization of Human Brain Network Modulated by Driving Mental Fatigue","volume":"21","author":"Zhao","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_7","unstructured":"Rifkin, H. (1987). Entropy: A New World View, Shanghai Translation Publishing House."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Min, J., Wang, P., and Hu, J. (2017). Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0188756"},{"key":"ref_9","unstructured":"Ye, B.G. (2019). Research on Recognition Method of Fatigue Driving State Based on KPCA, Nanchang University."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1002\/hbm.24393","article-title":"Changes in EEG multiscale entropy and power-law frequency scaling during the human sleep cycle","volume":"40","author":"Vladimir","year":"2019","journal-title":"Hum. Brain Mapp."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"108691","DOI":"10.1016\/j.jneumeth.2020.108691","article-title":"Constructing Multi-scale Entropy Based on the Empirical Mode Decomposition(EMD) and its Application in Recognizing Driving Fatigue","volume":"341","author":"Zou","year":"2020","journal-title":"J. Neurosci. Methods"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.neucom.2013.11.009","article-title":"Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine","volume":"133","author":"Kumar","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_13","first-page":"R789","article-title":"Sample entropy analysis of neonatal heart rate variability","volume":"283","author":"Lake","year":"2002","journal-title":"Am. J. Physiol."},{"key":"ref_14","first-page":"339","article-title":"Wavelet transform and sample entropy feature extraction methods for EEG signals","volume":"7","author":"Zhang","year":"2012","journal-title":"CAAI Trans. Intell. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2297","DOI":"10.1073\/pnas.88.6.2297","article-title":"Approximate entropy as a measure of system complexity","volume":"88","author":"Pincus","year":"1991","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_16","first-page":"116","article-title":"EEG Emotional Recognition Based on Nonlinear Global Features and Spectral Features","volume":"54","author":"Sun","year":"2018","journal-title":"J. Comput. Eng. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"066138","DOI":"10.1103\/PhysRevE.69.066138","article-title":"Estimating mutual information","volume":"69","author":"Kraskov","year":"2004","journal-title":"Phys. Rev. E Stat. Nonlinear Soft Matter Phys."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compbiomed.2018.12.005","article-title":"Detecting synchrony in EEG: A comparative study of functional connectivity measures","volume":"105","author":"Hanieh","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pearson, K. (1992). On the Criterion that a Given System of Deviations from the Probable in the Case of a Correlated System of Variables is Such that it Can be Reasonably Supposed to have Arisen from Random Sampling. Breakthroughs in Statistics, Springer.","DOI":"10.1007\/978-1-4612-4380-9_2"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.sigpro.2008.07.005","article-title":"Correntropy as a novel measure for nonlinearity tests","volume":"89","author":"Gunduz","year":"2009","journal-title":"Signal Process."},{"key":"ref_21","unstructured":"Silverman, B.M. (1996). Destiny Estimation for Statistics and Data Analysis, CRC Press."},{"key":"ref_22","unstructured":"Guo, H. (2013). Analysis and Classification of Abnormal Topological Attributes of Resting Function Network in Depression, Taiyuan University of Technology."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1038\/30918","article-title":"Collective dynamics of \u201csmall-world\u201d networks","volume":"393","author":"Watts","year":"1998","journal-title":"Nature"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1098\/rspb.2005.3354","article-title":"The brainstem reticular formation is a small-world, not scale-free, network","volume":"273","author":"Humphries","year":"2006","journal-title":"Proc. R. Soc. B Biol. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"290","DOI":"10.5486\/PMD.1959.6.3-4.12","article-title":"On random graphs","volume":"6","author":"Erdos","year":"1959","journal-title":"Publ. Math. Debrecen"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Mu, Z.D., Hu, J.F., and Min, J.L. (2017). Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features. Appl. Sci., 7.","DOI":"10.3390\/app7020150"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/11\/1234\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:26:56Z","timestamp":1760178416000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/11\/1234"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,30]]},"references-count":26,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["e22111234"],"URL":"https:\/\/doi.org\/10.3390\/e22111234","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2020,10,30]]}}}