Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/89299
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dc.contributorSchool of Nursingen_US
dc.creatorXie, Len_US
dc.creatorDeng, Zen_US
dc.creatorXu, Pen_US
dc.creatorChoi, KSen_US
dc.creatorWang, Sen_US
dc.date.accessioned2021-03-08T03:42:01Z-
dc.date.available2021-03-08T03:42:01Z-
dc.identifier.issn2168-2267en_US
dc.identifier.urihttp://hdl.handle.net/10397/89299-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.rightsThe following publication L. Xie, Z. Deng, P. Xu, K. -S. Choi and S. Wang, "Generalized Hidden-Mapping Transductive Transfer Learning for Recognition of Epileptic Electroencephalogram Signals," in IEEE Transactions on Cybernetics, vol. 49, no. 6, pp. 2200-2214, June 2019 is available at https://doi.org/10.1109/TCYB.2018.2821764.en_US
dc.subjectGeneralized hidden-mappingen_US
dc.subjectRecognition of electroencephalogram (EEG) signalsen_US
dc.subjectTransductive transfer learningen_US
dc.titleGeneralized hidden-mapping transductive transfer learning for recognition of epileptic electroencephalogram signalsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage2200en_US
dc.identifier.epage2214en_US
dc.identifier.volume49en_US
dc.identifier.issue6en_US
dc.identifier.doi10.1109/TCYB.2018.2821764en_US
dcterms.abstractElectroencephalogram (EEG) signal identification based on intelligent models is an important means in epilepsy detection. In the recognition of epileptic EEG signals, traditional intelligent methods usually assume that the training dataset and testing dataset have the same distribution, and the data available for training are adequate. However, these two conditions cannot always be met in practice, which reduces the ability of the intelligent recognition model obtained in detecting epileptic EEG signals. To overcome this issue, an effective strategy is to introduce transfer learning in the construction of the intelligent models, where knowledge is learned from the related scenes (source domains) to enhance the performance of model trained in the current scene (target domain). Although transfer learning has been used in EEG signal identification, many existing transfer learning techniques are designed only for a specific intelligent model, which limit their applicability to other classical intelligent models. To extend the scope of application, the generalized hidden-mapping transductive learning method is proposed to realize transfer learning for several classical intelligent models, including feedforward neural networks, fuzzy systems, and kernelized linear models. These intelligent models can be trained effectively by the proposed method even though the data available are insufficient for model training, and the generalization abilities of the trained model is also enhanced by transductive learning. A number of experiments are carried out to demonstrate the effectiveness of the proposed method in epileptic EEG recognition. The results show that the method is highly competitive or superior to some existing state-of-the-art methods.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on cybernetics, June 2019, v. 49, no. 6, p. 2200-2214en_US
dcterms.isPartOfIEEE transactions on cyberneticsen_US
dcterms.issued2019-06-
dc.identifier.isiWOS:000463030000019-
dc.identifier.pmid29993945-
dc.identifier.eissn2168-2275en_US
dc.description.validate202103 bcrcen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0597-n21-
dc.identifier.SubFormID464-
dc.description.fundingSourceRGCen_US
dc.description.fundingTextPolyU 152040/16Een_US
dc.description.pubStatusPublisheden_US
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