{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T11:22:35Z","timestamp":1774783355045,"version":"3.50.1"},"reference-count":82,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176105"],"award-info":[{"award-number":["62176105"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010014","name":"Six Talent Peaks Project in Jiangsu Province","doi-asserted-by":"publisher","award":["XYDXX-056"],"award-info":[{"award-number":["XYDXX-056"]}],"id":[{"id":"10.13039\/501100010014","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010023","name":"Natural Science Research of Jiangsu Higher Education Institutions of China","doi-asserted-by":"publisher","award":["24KJB520039"],"award-info":[{"award-number":["24KJB520039"]}],"id":[{"id":"10.13039\/501100010023","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.neucom.2026.133383","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T09:31:59Z","timestamp":1773826319000},"page":"133383","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A novel TSK fuzzy system incorporating multi-view collaborative transfer learning for personalized epileptic EEG detection"],"prefix":"10.1016","volume":"681","author":[{"given":"Andong","family":"Li","sequence":"first","affiliation":[]},{"given":"Zhaohong","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Qiongdan","family":"Lou","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.133383_bib1","unstructured":"W.H. Organization, Epilepsy, World Health Organization, (World Health Organization2024)."},{"key":"10.1016\/j.neucom.2026.133383_bib2","doi-asserted-by":"crossref","first-page":"2450","DOI":"10.1109\/TNSRE.2024.3421648","article-title":"Evaluation of acupuncture efficacy in modulating brain activity with periodic-aperiodic EEG measurements","volume":"32","author":"Yu","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.neucom.2026.133383_bib3","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1109\/TNSRE.2018.2828143","article-title":"Modulation of spectral power and functional connectivity in human brain by acupuncture stimulation","volume":"26","author":"Yu","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.neucom.2026.133383_bib4","first-page":"3740","article-title":"Frequency domain analysis of sleep EEG for visualization and automated state detection","author":"Vivaldi","year":"2006","journal-title":"Int. Conf. IEEE Eng. Med. Biol. Soc."},{"key":"10.1016\/j.neucom.2026.133383_bib5","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/TNSRE.2021.3076234","article-title":"An Attention-based deep learning approach for sleep stage classification with single-channel EEG","volume":"29","author":"Eldele","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.neucom.2026.133383_bib6","doi-asserted-by":"crossref","first-page":"4147","DOI":"10.1109\/JBHI.2025.3530922","article-title":"Neural manifold decoder for acupuncture stimulations with representation learning: an acupuncture-brain interface","volume":"29","author":"Yu","year":"2025","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"10.1016\/j.neucom.2026.133383_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.130086","article-title":"Topology analysis of EEG-based functional brain network after stroke","volume":"637","author":"Xi","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133383_bib8","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/TFUZZ.2019.2903753","article-title":"Supervised network-based fuzzy learning of EEG signals for Alzheimer's disease identification","volume":"28","author":"Yu","year":"2020","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.neucom.2026.133383_bib9","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1016\/0013-4694(82)90038-4","article-title":"Automatic recognition of epileptic seizures in the EEG","volume":"54","author":"Gotman","year":"1982","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"10.1016\/j.neucom.2026.133383_bib10","doi-asserted-by":"crossref","first-page":"2146","DOI":"10.1109\/TNSRE.2017.2697920","article-title":"Real-time epileptic seizure detection using EEG","volume":"25","author":"Vidyaratne","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.neucom.2026.133383_bib11","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2019.101702","article-title":"A review of feature extraction and performance evaluation in epileptic seizure detection using EEG","volume":"57","author":"Boonyakitanont","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.neucom.2026.133383_bib12","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/JBHI.2017.2654479","article-title":"Epileptic seizure classification of EEGs using time\u2013frequency analysis based multiscale radial basis functions","volume":"22","author":"Li","year":"2018","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"10.1016\/j.neucom.2026.133383_bib13","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/51.566156","article-title":"Applying time-frequency analysis to seizure EEG activity","volume":"16","author":"Blanco","year":"1997","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"10.1016\/j.neucom.2026.133383_bib14","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1109\/TBME.2017.2650259","article-title":"A multivariate approach for patient-specific EEG seizure detection using empirical wavelet transform","volume":"64","author":"Bhattacharyya","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.neucom.2026.133383_bib15","doi-asserted-by":"crossref","DOI":"10.3390\/brainsci9050115","article-title":"Epilepsy detection by using scalogram based convolutional neural network from EEG signals","volume":"9","author":"T\u00fcrk","year":"2019","journal-title":"Brain Sci."},{"key":"10.1016\/j.neucom.2026.133383_bib16","doi-asserted-by":"crossref","first-page":"10499","DOI":"10.1016\/j.eswa.2011.02.110","article-title":"Classification of electroencephalogram signals with combined time and frequency features","volume":"38","author":"Iscan","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.133383_bib17","doi-asserted-by":"crossref","first-page":"2585","DOI":"10.1109\/TCYB.2014.2311014","article-title":"Generalized hidden-mapping ridge regression, knowledge-leveraged inductive transfer learning for neural networks, fuzzy systems and kernel methods","volume":"44","author":"Deng","year":"2014","journal-title":"IEEE Trans. Cyber"},{"key":"10.1016\/j.neucom.2026.133383_bib18","doi-asserted-by":"crossref","first-page":"1648","DOI":"10.1016\/j.clinph.2009.07.002","article-title":"A fuzzy rule-based system for epileptic seizure detection in intracranial EEG","volume":"120","author":"Aarabi","year":"2009","journal-title":"Clin. Neurophysiol."},{"key":"10.1016\/j.neucom.2026.133383_bib19","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.1109\/TNSRE.2018.2850308","article-title":"Transductive joint-knowledge-transfer TSK FS for recognition of epileptic EEG signals","volume":"26","author":"Deng","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.neucom.2026.133383_bib20","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1145\/1543834.1543860","article-title":"Classification of EEG signals using relative wavelet energy and artificial neural networks","author":"Guo","year":"2009","journal-title":"Proc. first ACM\/SIGEVO Summit Genet. Evolut. Comput."},{"key":"10.1016\/j.neucom.2026.133383_bib21","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1142\/S0129065710002334","article-title":"Automatic identification of epileptic and background EEG signals using frequency domain parameters","volume":"20","author":"Faust","year":"2010","journal-title":"Int. J. Neural Syst."},{"key":"10.1016\/j.neucom.2026.133383_bib22","doi-asserted-by":"crossref","first-page":"10425","DOI":"10.1016\/j.eswa.2011.02.118","article-title":"Automatic feature extraction using genetic programming: an application to epileptic EEG classification","volume":"38","author":"Guo","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.133383_bib23","doi-asserted-by":"crossref","DOI":"10.1142\/S012906571250027X","article-title":"Application of empirical mode decomposition (EMD) for automated detection of epilepsy using EEG signals","volume":"22","author":"Martis","year":"2012","journal-title":"Int. J. Neural Syst."},{"key":"10.1016\/j.neucom.2026.133383_bib24","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.bspc.2011.07.007","article-title":"Automated diagnosis of epileptic EEG using entropies","volume":"7","author":"Acharya","year":"2012","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.neucom.2026.133383_bib25","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1016\/j.eswa.2018.08.031","article-title":"An end-to-end deep learning approach to MI-EEG signal classification for BCIs","volume":"114","author":"Dose","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.133383_bib26","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2020.117021","article-title":"Machine-learning-based diagnostics of EEG pathology","volume":"220","author":"Gemein","year":"2020","journal-title":"NeuroImage"},{"key":"10.1016\/j.neucom.2026.133383_bib27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13755-020-00129-1","article-title":"Automated epilepsy detection techniques from electroencephalogram signals: a review study","volume":"8","author":"Supriya","year":"2020","journal-title":"Health Inf. Sci. Syst."},{"key":"10.1016\/j.neucom.2026.133383_bib28","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1109\/LSP.2009.2022557","article-title":"Composite common spatial pattern for subject-to-subject transfer","volume":"16","author":"Kang","year":"2009","journal-title":"IEEE Signal Process Lett."},{"key":"10.1016\/j.neucom.2026.133383_bib29","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TFUZZ.2016.2637405","article-title":"Recognition of epileptic EEG signals using a novel multiview TSK fuzzy system","volume":"25","author":"Jiang","year":"2017","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.neucom.2026.133383_bib30","article-title":"Dynamic time warping-based transfer learning for improving common spatial patterns in brain-computer interface","volume":"17","author":"Azab","year":"2019","journal-title":"J. Neural Eng."},{"key":"10.1016\/j.neucom.2026.133383_bib31","doi-asserted-by":"crossref","first-page":"2200","DOI":"10.1109\/TCYB.2018.2821764","article-title":"Generalized hidden-mapping transductive transfer learning for recognition of epileptic electroencephalogram signals","volume":"49","author":"Xie","year":"2019","journal-title":"IEEE Trans. Cyber"},{"key":"10.1016\/j.neucom.2026.133383_bib32","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.1109\/TFUZZ.2018.2871005","article-title":"Multiview fuzzy logic system with the cooperation between visible and hidden views","volume":"27","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.neucom.2026.133383_bib33","doi-asserted-by":"crossref","unstructured":"A. Blum, T. Mitchell, Combining labeled and unlabeled data with co-training, Proceedings of the 11th Annual Conference on Computational Learning Theory 1998).","DOI":"10.1145\/279943.279962"},{"key":"10.1016\/j.neucom.2026.133383_bib34","first-page":"27","article-title":"Learning the kernel matrix with semidefinite programming","volume":"5","author":"Lanckriet","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.neucom.2026.133383_bib35","unstructured":"S. Akaho, A kernel method for canonical correlation analysis, arXiv preprint cs\/0609071, (2006)."},{"key":"10.1016\/j.neucom.2026.133383_bib36","doi-asserted-by":"crossref","first-page":"172352","DOI":"10.1109\/ACCESS.2020.3024580","article-title":"Seizure prediction using multi-view features and improved convolutional gated recurrent network","volume":"8","author":"Tang","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.133383_bib37","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/JBHI.2018.2871678","article-title":"A multi-view deep learning framework for EEG seizure detection","volume":"23","author":"Yuan","year":"2019","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"10.1016\/j.neucom.2026.133383_bib38","doi-asserted-by":"crossref","first-page":"170352","DOI":"10.1109\/ACCESS.2019.2955285","article-title":"Epileptic seizure prediction with multi-view convolutional neural networks","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.133383_bib39","doi-asserted-by":"crossref","first-page":"1962","DOI":"10.1109\/TNSRE.2019.2940485","article-title":"Deep multi-view feature learning for EEG-based epileptic seizure detection","volume":"27","author":"Tian","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.neucom.2026.133383_bib40","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/0167-8760(84)90045-X","article-title":"Correlation and coherence analysis of the EEG: a selective tutorial review","volume":"1","author":"Shaw","year":"1984","journal-title":"Int. J. Psychophysiol."},{"key":"10.1016\/j.neucom.2026.133383_bib41","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.neunet.2020.01.017","article-title":"EEG based multi-class seizure type classification using convolutional neural network and transfer learning","volume":"124","author":"Raghu","year":"2020","journal-title":"Neural Netw."},{"key":"10.1016\/j.neucom.2026.133383_bib42","doi-asserted-by":"crossref","first-page":"2390","DOI":"10.1109\/TBME.2018.2889705","article-title":"Riemannian procrustes analysis: transfer learning for brain\u2013computer interfaces","volume":"66","author":"Rodrigues","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.neucom.2026.133383_bib43","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1109\/TNSRE.2019.2904708","article-title":"Recognition of multiclass epileptic EEG signals based on knowledge and label space inductive transfer","volume":"27","author":"Jiang","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.neucom.2026.133383_bib44","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/TCBB.2020.2973978","article-title":"Cross-domain classification model with knowledge utilization maximization for recognition of epileptic EEG signals","volume":"18","author":"Xia","year":"2020","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf"},{"key":"10.1016\/j.neucom.2026.133383_bib45","unstructured":"S.J. Pan, J.T. Kwok, Q. Yang, Transfer Learning via Dimensionality Reduction, Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence 2008)."},{"key":"10.1016\/j.neucom.2026.133383_bib46","first-page":"1541","article-title":"Heterogeneous domain adaptation using manifold alignment, IJCAI Proceedings-International","author":"Wang","year":"2011","journal-title":"Jt. Conf. Artif. Intell."},{"key":"10.1016\/j.neucom.2026.133383_bib47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0020-7373(75)80002-2","article-title":"An experiment in linguistic synthesis with a fuzzy logic controller","volume":"7","author":"Mamdani","year":"1975","journal-title":"Int. J. Man Mach. Stud."},{"key":"10.1016\/j.neucom.2026.133383_bib48","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.eswa.2006.08.020","article-title":"A TSK type fuzzy rule based system for stock price prediction","volume":"34","author":"Chang","year":"2008","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.133383_bib49","first-page":"502","article-title":"Recognition of large-Scale ncRNA data using a novel multitask cross-learning 0-order TSK fuzzy classifier","volume":"10","author":"Jiang","year":"2020","journal-title":"J. Med. Imaging Health Inf."},{"key":"10.1016\/j.neucom.2026.133383_bib50","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.1109\/TFUZZ.2018.2871005","article-title":"Multi-view fuzzy logic system with the cooperation between visible and hidden views","volume":"27","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.neucom.2026.133383_bib51","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1504\/IJBIC.2019.100147","article-title":"Intelligent diagnosis of cardiac valve calcification in ESRD patients with peritoneal dialysis based on improved Takagi-Sugeno-Kang fuzzy system","volume":"13","author":"Xue","year":"2019","journal-title":"Int. J. BioInspired Comput."},{"key":"10.1016\/j.neucom.2026.133383_bib52","doi-asserted-by":"crossref","first-page":"3881","DOI":"10.1007\/s10489-019-01439-y","article-title":"A recurrent TSK interval type-2 fuzzy neural networks control with online structure and parameter learning for mobile robot trajectory tracking","volume":"49","author":"Bencherif","year":"2019","journal-title":"Appl. Intell."},{"key":"10.1016\/j.neucom.2026.133383_bib53","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/TSMC.1985.6313399","article-title":"Fuzzy identification of systems and its applications to modeling and control","volume":"15","author":"Takagi","year":"1985","journal-title":"IEEE Trans. Syst. Man Cybern. SMC"},{"key":"10.1016\/j.neucom.2026.133383_bib54","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1109\/91.995115","article-title":"Type-2 fuzzy sets made simple","volume":"10","author":"Mendel","year":"2002","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.neucom.2026.133383_bib55","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2022.102452","article-title":"Using type-2 fuzzy ontology to improve semantic interoperability for healthcare and diagnosis of depression","volume":"135","author":"Ghorbani","year":"2023","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.neucom.2026.133383_bib56","doi-asserted-by":"crossref","first-page":"5508","DOI":"10.1109\/TFUZZ.2024.3412197","article-title":"Fuzzy-centric fog\u2013cloud inspired deep interval Bi-LSTM healthcare framework for predicting yellow fever outbreak","volume":"32","author":"Verma","year":"2024","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.neucom.2026.133383_bib57","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1109\/TCSS.2022.3164889","article-title":"Interval Type-2 fuzzy risk evaluation and prevention for parallel breast cancer treatment system","volume":"10","author":"Mo","year":"2023","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"10.1016\/j.neucom.2026.133383_bib58","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1109\/5.364485","article-title":"Fuzzy logic systems for engineering: a tutorial","volume":"83","author":"Mendel","year":"1995","journal-title":"Proc. IEEE"},{"key":"10.1016\/j.neucom.2026.133383_bib59","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1109\/TFUZZ.2019.2958299","article-title":"Transfer representation learning with TSK fuzzy system","volume":"29","author":"Xu","year":"2021","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.neucom.2026.133383_bib60","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1109\/TCYB.2014.2334595","article-title":"Collaborative fuzzy clustering from multiple weighted views","volume":"45","author":"Jiang","year":"2015","journal-title":"IEEE Trans. Cyber"},{"key":"10.1016\/j.neucom.2026.133383_bib61","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1109\/TFUZZ.2015.2501438","article-title":"Takagi\u2013Sugeno\u2013Kang transfer learning fuzzy logic system for the adaptive recognition of epileptic electroencephalogram signals","volume":"24","author":"Yang","year":"2016","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.neucom.2026.133383_bib62","doi-asserted-by":"crossref","first-page":"837","DOI":"10.3389\/fnins.2020.00837","article-title":"An intelligence EEG signal recognition method via noise insensitive TSK fuzzy system based on interclass competitive learning","volume":"14","author":"Ni","year":"2020","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.neucom.2026.133383_bib63","first-page":"513","article-title":"A kernel method for the two-sample-problem","author":"Bernhard","year":"2007","journal-title":"Adv. Neural Inf. Process. Syst. 19 Proc. 2006 Conf."},{"key":"10.1016\/j.neucom.2026.133383_bib64","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/0098-3004(84)90020-7","article-title":"FCM: the fuzzy c-means clustering algorithm","volume":"10","author":"Bezdek","year":"1984","journal-title":"Comput. Geosci."},{"key":"10.1016\/j.neucom.2026.133383_bib65","doi-asserted-by":"crossref","first-page":"4470","DOI":"10.1109\/TFUZZ.2024.3401109","article-title":"Comprehensive study on a Fuzzy parameter strategy of zeroing neural network for time-variant complex sylvester equation","volume":"32","author":"Kong","year":"2024","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.neucom.2026.133383_bib66","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/0013-4694(70)90143-4","article-title":"EEG analysis based on time domain properties","volume":"29","author":"Hjorth","year":"1970","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"10.1016\/j.neucom.2026.133383_bib67","doi-asserted-by":"crossref","first-page":"3774","DOI":"10.1111\/ejn.12749","article-title":"Spectrotemporal dynamics of the EEG during working memory encoding and maintenance predicts individual behavioral capacity","volume":"40","author":"Bashivan","year":"2014","journal-title":"Eur. J. Neurosci."},{"key":"10.1016\/j.neucom.2026.133383_bib68","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.bspc.2014.03.007","article-title":"Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM","volume":"13","author":"Fu","year":"2014","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.neucom.2026.133383_bib69","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.seizure.2015.01.012","article-title":"Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis","volume":"26","author":"Faust","year":"2015","journal-title":"Seizure"},{"key":"10.1016\/j.neucom.2026.133383_bib70","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1109\/TNNLS.2015.2442256","article-title":"Alternative multiview maximum entropy discrimination","volume":"27","author":"Chao","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.neucom.2026.133383_bib71","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"398","article-title":"mulEEG: a multi-view representation learning on EEG signals","author":"Kumar","year":"2022"},{"key":"10.1016\/j.neucom.2026.133383_bib72","first-page":"1","article-title":"Multimodal polysomnography based automatic sleep stage classification via multiview fusion network","volume":"73","author":"Lin","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.neucom.2026.133383_bib73","doi-asserted-by":"crossref","unstructured":"W. Dai, Q. Yang, G.-R. Xue, Y. Yu, Boosting for transfer learning, the 24th international conference on Machine learning, (Association for Computing Machinery, Corvalis, Oregon, USA, 2007), pp. 193\u2013200.","DOI":"10.1145\/1273496.1273521"},{"key":"10.1016\/j.neucom.2026.133383_bib74","doi-asserted-by":"crossref","DOI":"10.1088\/1741-2552\/aace8c","article-title":"EEGNet: a compact convolutional neural network for EEG-based brain\u2013computer interfaces","volume":"15","author":"Lawhern","year":"2018","journal-title":"J. Neural Eng."},{"key":"10.1016\/j.neucom.2026.133383_bib75","doi-asserted-by":"crossref","first-page":"103531","DOI":"10.1109\/ACCESS.2025.3578991","article-title":"A comprehensive review of EEG-based seizure detection techniques","volume":"13","author":"Jebaraj","year":"2025","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.133383_bib76","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128644","article-title":"EEG-based epileptic seizure detection using deep learning techniques: a survey","volume":"610","author":"Xu","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133383_bib77","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065723500429","article-title":"Epileptic EEG classification via graph transformer network","volume":"33","author":"Lian","year":"2023","journal-title":"Int. J. Neural Syst."},{"key":"10.1016\/j.neucom.2026.133383_bib78","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065723500314","article-title":"Hybrid attention network for epileptic EEG classification","volume":"33","author":"Zhao","year":"2023","journal-title":"Int. J. Neural Syst."},{"key":"10.1016\/j.neucom.2026.133383_bib79","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065724500552","article-title":"Cross-subject seizure detection via unsupervised domain-adaptation","volume":"34","author":"Wang","year":"2024","journal-title":"Int. J. Neural Syst."},{"key":"10.1016\/j.neucom.2026.133383_bib80","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1038\/s41598-023-43328-y","article-title":"A ResNet-LSTM hybrid model for predicting epileptic seizures using a pretrained model with supervised contrastive learning","volume":"14","author":"Lee","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.neucom.2026.133383_bib81","doi-asserted-by":"crossref","first-page":"184312","DOI":"10.1109\/ACCESS.2025.3625784","article-title":"Enhanced detection of epileptic seizure using hybrid framework of slantlet transform and spiking neural network model","volume":"13","author":"Tripathy","year":"2025","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.133383_bib82","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.fss.2004.06.011","article-title":"How to determine the minimum number of fuzzy rules to achieve given accuracy: a computational geometric approach to SISO case","volume":"150","author":"Wan","year":"2005","journal-title":"Fuzzy Sets Syst."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226007800?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226007800?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T07:36:45Z","timestamp":1774769805000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226007800"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":82,"alternative-id":["S0925231226007800"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133383","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A novel TSK fuzzy system incorporating multi-view collaborative transfer learning for personalized epileptic EEG detection","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133383","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"133383"}}