{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T17:04:27Z","timestamp":1765040667264,"version":"3.37.3"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2016,9,20]],"date-time":"2016-09-20T00:00:00Z","timestamp":1474329600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 61172134","No.61671197","No.61601162"],"award-info":[{"award-number":["No. 61172134","No.61671197","No.61601162"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2018,5]]},"DOI":"10.1007\/s00521-016-2594-z","type":"journal-article","created":{"date-parts":[[2016,9,20]],"date-time":"2016-09-20T10:05:32Z","timestamp":1474365932000},"page":"857-863","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["An entropy fusion method for feature extraction of EEG"],"prefix":"10.1007","volume":"29","author":[{"given":"Shunfei","family":"Chen","sequence":"first","affiliation":[]},{"given":"Zhizeng","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Haitao","family":"Gan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,9,20]]},"reference":[{"key":"2594_CR1","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.bspc.2015.04.007","volume":"20","author":"T Nguyen","year":"2015","unstructured":"Nguyen T, Khosravi A, Creighton D (2015) Fuzzy system with tabu search learning for classification of motor imagery data. Biomed Signal Process Control 20:61\u201370","journal-title":"Biomed Signal Process Control"},{"issue":"9","key":"2594_CR2","doi-asserted-by":"crossref","first-page":"4370","DOI":"10.1016\/j.eswa.2015.01.036","volume":"42","author":"T Nguyen","year":"2015","unstructured":"Nguyen T, Khosravi A, Creighton D et al (2015) EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems. Expert Syst Appl 42(9):4370\u20134380","journal-title":"Expert Syst Appl"},{"key":"2594_CR3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eswa.2016.04.011","volume":"59","author":"AA Torres-Garc\u00eda","year":"2016","unstructured":"Torres-Garc\u00eda AA, Reyes-Garc\u00eda CA, Villase\u00f1or-Pineda L et al (2016) Implementing a fuzzy inference system in a multi-objective EEG channel selection model for imagined speech classification. Expert Syst Appl 59:1\u201312","journal-title":"Expert Syst Appl"},{"key":"2594_CR4","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.cmpb.2016.04.023","volume":"132","author":"S Liang","year":"2016","unstructured":"Liang S, Choi KS, Qin J, Pang WM, Wang Q, Heng PA (2016) Improving the discrimination of hand motor imagery via virtual reality based visual guidance. Comput Methods Programs Biomed 132:63\u201374","journal-title":"Comput Methods Programs Biomed"},{"key":"2594_CR5","doi-asserted-by":"crossref","unstructured":"Gaur P, Pachori RB, Wang H et al (2015) An empirical mode decomposition based filtering method for classification of motor-imagery EEG signals for enhancing brain-computer interface. In: Proceeding of 2015 international joint conference on neural networks (IJCNN) IEEE, pp 1\u20137","DOI":"10.1109\/IJCNN.2015.7280754"},{"key":"2594_CR6","doi-asserted-by":"crossref","unstructured":"Marchesotti S, Bassolino M, Serino A et al (2016) Quantifying the role of motor imagery in brain-machine interfaces. Scientific reports, p 6","DOI":"10.1038\/srep24076"},{"issue":"7\u20138","key":"2594_CR7","doi-asserted-by":"crossref","first-page":"1931","DOI":"10.1007\/s00521-012-1244-3","volume":"23","author":"S Hu","year":"2013","unstructured":"Hu S, Tian Q, Cao Y et al (2013) Motor imagery classification based on joint regression model and spectral power. Neural Comput Appl 23(7\u20138):1931\u20131936","journal-title":"Neural Comput Appl"},{"issue":"21","key":"2594_CR8","doi-asserted-by":"crossref","first-page":"7344","DOI":"10.1016\/j.eswa.2015.05.028","volume":"42","author":"L Chen","year":"2015","unstructured":"Chen L, Zhao Y, Zhang J et al (2015) Automatic detection of alertness\/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning. Expert Syst Appl 42(21):7344\u20137355","journal-title":"Expert Syst Appl"},{"issue":"12","key":"2594_CR9","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1007\/s10439-009-9795-x","volume":"37","author":"S Ayd\u0131n","year":"2009","unstructured":"Ayd\u0131n S, Sarao\u011flu HM, Kara S (2009) Log energy entropy-based EEG classification with multilayer neural networks in seizure. Ann Biomed Eng 37(12):2626\u20132630","journal-title":"Ann Biomed Eng"},{"issue":"10","key":"2594_CR10","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1134\/S1064226915100113","volume":"60","author":"OS Sushkova","year":"2015","unstructured":"Sushkova OS, Gabova AV, Karabanov AV et al (2015) Time\u2013frequency analysis of simultaneous measurements of electroencephalograms, electromyograms, and mechanical tremor under Parkinson disease. J Commun Technol Electron 60(10):1109\u20131116","journal-title":"J Commun Technol Electron"},{"key":"2594_CR11","doi-asserted-by":"crossref","unstructured":"Xiao D, Mu Z, Hu J (2009) Classification of motor imagery EEG signals based on energy entropy. In: Proceeding of 2009 international symposium on intelligent ubiquitous computing and education, pp 61\u201364","DOI":"10.1109\/IUCE.2009.57"},{"issue":"5","key":"2594_CR12","first-page":"1","volume":"28","author":"Z Rui","year":"2015","unstructured":"Rui Z, Peng X, Rui C et al (2015) Predicting inter-session performance of SMR-based brain-computer interface using the spectral entropy of resting-state EEG. Brain Topogr 28(5):1\u201311","journal-title":"Brain Topogr"},{"issue":"15","key":"2594_CR13","doi-asserted-by":"crossref","first-page":"3291","DOI":"10.1103\/PhysRevLett.81.3291","volume":"81","author":"P Tass","year":"1998","unstructured":"Tass P, Rosenblum MG, Weule J et al (1998) Detection of n:m phase locking from noisy data: application to magnetoencephalography. Phys Rev Lett 81(15):3291","journal-title":"Phys Rev Lett"},{"issue":"1","key":"2594_CR14","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.jneumeth.2010.08.030","volume":"193","author":"L Guo","year":"2010","unstructured":"Guo L, Rivero D, Pazos A (2010) Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J Neurosci Methods 193(1):156\u2013163","journal-title":"J Neurosci Methods"},{"issue":"20","key":"2594_CR15","first-page":"1844","volume":"8","author":"Z Zhang","year":"2013","unstructured":"Zhang Z, Zhou Y, Chen Z et al (2013) Approximate entropy and support vector machines for electroencephalogram signal classification. Neural Regen Res 8(20):1844","journal-title":"Neural Regen Res"},{"key":"2594_CR16","doi-asserted-by":"crossref","unstructured":"Chen Z, Zhou H, Zhao L (2011) Decoding human right and left hand motor imagery from EEG single trials using sample entropy. In: Proceeding of 2011 international conference on IEEE electronics and optoelectronics (ICEOE), pp V4-353\u2013V4-356","DOI":"10.1109\/ICEOE.2011.6013503"},{"issue":"2","key":"2594_CR17","doi-asserted-by":"crossref","first-page":"2027","DOI":"10.1016\/j.eswa.2007.12.065","volume":"36","author":"H Ocak","year":"2009","unstructured":"Ocak H (2009) Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst Appl 36(2):2027\u20132036","journal-title":"Expert Syst Appl"},{"issue":"2","key":"2594_CR18","doi-asserted-by":"crossref","first-page":"669","DOI":"10.3390\/e17020669","volume":"17","author":"R Sharma","year":"2015","unstructured":"Sharma R, Pachori RB, Acharya UR (2015) Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals. Entropy 17(2):669\u2013691","journal-title":"Entropy"},{"issue":"03","key":"2594_CR19","doi-asserted-by":"crossref","first-page":"1350009","DOI":"10.1142\/S0129065713500093","volume":"23","author":"UR Acharya","year":"2013","unstructured":"Acharya UR, Yanti R, Zheng JW et al (2013) Automated diagnosis of epilepsy using CWT, HOS and texture parameters. Int J Neural Syst 23(03):1350009","journal-title":"Int J Neural Syst"},{"issue":"3","key":"2594_CR20","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.cmpb.2005.06.012","volume":"80","author":"N Kannathal","year":"2005","unstructured":"Kannathal N, Choo ML, Acharya UR et al (2005) Entropies for detection of epilepsy in EEG. Comput Methods Programs Biomed 80(3):187\u2013194","journal-title":"Comput Methods Programs Biomed"},{"key":"2594_CR21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fnhum.2015.00501","volume":"9","author":"K Sakihara","year":"2015","unstructured":"Sakihara K, Inagaki M (2015) Mu rhythm desynchronization by tongue thrust observation. Front Hum Neurosci 9:1\u201310","journal-title":"Front Hum Neurosci"},{"issue":"18","key":"2594_CR22","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1097\/WNR.0000000000000494","volume":"26","author":"G Kwon","year":"2015","unstructured":"Kwon G, Kim MY, Lim S et al (2015) Frontoparietal EEG alpha-phase synchrony reflects differential attentional demands during word recall and oculomotor dual-tasks. NeuroReport 26(18):1161\u20131167","journal-title":"NeuroReport"},{"issue":"2","key":"2594_CR23","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1093\/brain\/awl241","volume":"130","author":"F Mormann","year":"2007","unstructured":"Mormann F, Andrzejak RG, Elger CE et al (2007) Seizure prediction: the long and winding road. Brain 130(2):314\u2013333","journal-title":"Brain"},{"key":"2594_CR24","unstructured":"Tuncay C (2010) Entropy analyses of spatiotemporal synchronizations in brain signals from patients with focal epilepsies. arXiv preprint. \n                        arXiv:1002.3552"},{"key":"2594_CR25","doi-asserted-by":"crossref","unstructured":"Bashar SK, Bhuiyan MIH (2015) Automatic feature selection based motor imagery movements detection scheme from EEG signals in the dual tree complex wavelet transform domain. In: Proceeding of 2015 IEEE international conference on telecommunications and photonics (ICTP), pp 1\u20135","DOI":"10.1109\/ICTP.2015.7427947"},{"key":"2594_CR26","unstructured":"Li X, Cui W, Li C (2012) Research on classification method of wavelet entropy and fuzzy neural networks for motor imagery EEG. In: Proceeding of 2012 IEEE international conference on modelling, identification & control (ICMIC), pp 478\u2013482"},{"key":"2594_CR27","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.knosys.2015.08.004","volume":"88","author":"UR Acharya","year":"2015","unstructured":"Acharya UR, Fujita H, Sudarshan VK et al (2015) Application of entropies for automated diagnosis of epilepsy using EEG signals: a review. Knowl Based Syst 88:85\u201396","journal-title":"Knowl Based Syst"},{"issue":"4","key":"2594_CR28","doi-asserted-by":"crossref","first-page":"3284","DOI":"10.1016\/j.eswa.2009.09.051","volume":"37","author":"SP Kumar","year":"2010","unstructured":"Kumar SP, Sriraam N, Benakop PG et al (2010) Entropies based detection of epileptic seizures with artificial neural network classifiers. Expert Syst Appl 37(4):3284\u20133291","journal-title":"Expert Syst Appl"},{"issue":"6","key":"2594_CR29","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","volume":"278","author":"JS Richman","year":"2000","unstructured":"Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278(6):H2039\u2013H2049","journal-title":"Am J Physiol Heart Circ Physiol"},{"issue":"1","key":"2594_CR30","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.eswa.2011.07.008","volume":"39","author":"N Nicolaou","year":"2012","unstructured":"Nicolaou N, Georgiou J (2012) Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Expert Syst Appl 39(1):202\u2013209","journal-title":"Expert Syst Appl"},{"key":"2594_CR31","unstructured":"http:\/\/bbci.de\/competition\/ii\/"},{"key":"2594_CR32","doi-asserted-by":"crossref","unstructured":"Wang L, Xu G, Yang S et al (2012) Motor imagery BCI research based on sample entropy and SVM. In: Proceeding of 2012 sixth international conference on electromagnetic field problems and applications (ICEF), pp 1\u20134","DOI":"10.1109\/ICEF.2012.6310370"},{"key":"2594_CR33","doi-asserted-by":"crossref","unstructured":"Imran SM, Talukdar MTF, Sakib SK et al (2014) Motor imagery EEG signal classification scheme based on wavelet domain statistical features. In: Proceeding of 2014 international conference on electrical engineering and information communication technology (ICEEICT), pp 1\u20134","DOI":"10.1109\/ICEEICT.2014.6919172"},{"key":"2594_CR34","doi-asserted-by":"crossref","unstructured":"Gupta SS, Soman S, Raj PG et al (2014) Improved classification of motor imagery datasets for BCI by using approximate entropy and WOSF features. In: Proceeding of 2014 international conference on signal processing and integrated networks (SPIN), pp 90\u201394","DOI":"10.1109\/SPIN.2014.6776928"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-016-2594-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-016-2594-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-016-2594-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,3,31]],"date-time":"2018-03-31T02:23:27Z","timestamp":1522463007000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-016-2594-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,9,20]]},"references-count":34,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2018,5]]}},"alternative-id":["2594"],"URL":"https:\/\/doi.org\/10.1007\/s00521-016-2594-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2016,9,20]]}}}