{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T16:52:35Z","timestamp":1786121555239,"version":"build-2736575974"},"reference-count":74,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100016079","name":"Science and Technology Department of Xinjiang Uygur Autonomous Region","doi-asserted-by":"publisher","award":["2025LQ01005"],"award-info":[{"award-number":["2025LQ01005"]}],"id":[{"id":"10.13039\/100016079","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.neucom.2026.134587","type":"journal-article","created":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T06:50:10Z","timestamp":1784962210000},"page":"134587","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Learning from semantic ambiguity: A dual-noise robust framework for EEG partial label emotion recognition"],"prefix":"10.1016","volume":"701","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0436-5209","authenticated-orcid":false,"given":"Yiming","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4936-0061","authenticated-orcid":false,"given":"Bin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8687-1657","authenticated-orcid":false,"given":"Lamei","family":"Di","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.134587_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.129605","article-title":"Deep learning-based depression recognition through facial expression: a systematic review","volume":"627","author":"Cao","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134587_bib0010","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s10796-024-10484-z","article-title":"Domain adaptation for fear of heights classification in a VR environment based on EEG and ECG","volume":"27","author":"Apicella","year":"2024","journal-title":"Inf. Syst. Front."},{"key":"10.1016\/j.neucom.2026.134587_bib0015","series-title":"The Twelfth International Conference on Learning Representations","article-title":"VBH-GNN: variational Bayesian heterogeneous graph neural networks for cross-subject emotion recognition","author":"Liu","year":"2024"},{"issue":"3","key":"10.1016\/j.neucom.2026.134587_bib0020","doi-asserted-by":"crossref","first-page":"2512","DOI":"10.1109\/TAFFC.2022.3170428","article-title":"GMSS: graph-based multi-task self-supervised learning for EEG emotion recognition","volume":"14","author":"Li","year":"2022","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"3","key":"10.1016\/j.neucom.2026.134587_bib0025","doi-asserted-by":"crossref","first-page":"2496","DOI":"10.1109\/TAFFC.2022.3164516","article-title":"Contrastive learning of subject-invariant EEG representations for cross-subject emotion recognition","volume":"14","author":"Shen","year":"2022","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"11","key":"10.1016\/j.neucom.2026.134587_bib0030","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3666002","article-title":"Research progress of EEG-based emotion recognition: a survey","volume":"56","author":"Wang","year":"2024","journal-title":"ACM Comput. Surv."},{"issue":"3","key":"10.1016\/j.neucom.2026.134587_bib0035","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1109\/TCYB.2018.2797176","article-title":"Emotionmeter: a multimodal framework for recognizing human emotions","volume":"49","author":"Zheng","year":"2018","journal-title":"IEEE Trans. Cybern."},{"issue":"1","key":"10.1016\/j.neucom.2026.134587_bib0040","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","article-title":"DEAP: a database for emotion analysis; using physiological signals","volume":"3","author":"Koelstra","year":"2011","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"108833","key":"10.1016\/j.neucom.2026.134587_bib0045","article-title":"Visual-to-EEG cross-modal knowledge distillation for continuous emotion recognition","volume":"130","author":"Zhang","year":"2022","journal-title":"Pattern Recognit."},{"issue":"3","key":"10.1016\/j.neucom.2026.134587_bib0050","doi-asserted-by":"crossref","first-page":"2238","DOI":"10.1109\/TAFFC.2022.3169001","article-title":"TSception: capturing temporal dynamics and spatial asymmetry from EEG for emotion recognition","volume":"14","author":"Ding","year":"2022","journal-title":"IEEE Trans. Affect. Comput."},{"key":"10.1016\/j.neucom.2026.134587_bib0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.132235","article-title":"EEG-based fusion approaches in multimodal emotion recognition: an in-depth review","volume":"666","author":"Hatipoglu Yilmaz","year":"2026","journal-title":"Neurocomputing"},{"issue":"3","key":"10.1016\/j.neucom.2026.134587_bib0060","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","article-title":"Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks","volume":"7","author":"Zheng","year":"2015","journal-title":"IEEE Trans. Auton. Ment. Dev."},{"key":"10.1016\/j.neucom.2026.134587_bib0065","series-title":"Proceedings of the 32nd ACM International Conference on Multimedia","first-page":"2204","article-title":"REmoNet: reducing emotional label noise via multi-regularized self-supervision","author":"Jiang","year":"2024"},{"issue":"4","key":"10.1016\/j.neucom.2026.134587_bib0070","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1511\/2001.28.344","article-title":"The nature of emotions","volume":"89","author":"Plutchik","year":"2001","journal-title":"Am. Sci."},{"key":"10.1016\/j.neucom.2026.134587_bib0075","first-page":"1501","article-title":"Learning from partial labels","volume":"12","author":"Cour","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.neucom.2026.134587_bib0080","series-title":"International Conference on Machine Learning","first-page":"11091","article-title":"Leveraged weighted loss for partial label learning","volume":"vol. 139","author":"Wen","year":"2021"},{"key":"10.1016\/j.neucom.2026.134587_bib0085","author":"Zhang"},{"key":"10.1016\/j.neucom.2026.134587_bib0090","series-title":"International Conference on Learning Representations","article-title":"PiCO: contrastive label disambiguation for partial label learning","author":"Wang","year":"2022"},{"key":"10.1016\/j.neucom.2026.134587_bib0095","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"15589","article-title":"Towards effective visual representations for partial-label learning","author":"Xia","year":"2023"},{"key":"10.1016\/j.neucom.2026.134587_bib0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.107635","article-title":"Real-PiCO: partial label learning with contrasting label disambiguation for EEG emotion recognition in real-world scenarios","volume":"105","author":"He","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.neucom.2026.134587_bib0105","series-title":"Advances in Neural Information Processing Systems","first-page":"27119","article-title":"Instance-dependent partial label learning","volume":"vol. 34","author":"Xu","year":"2021"},{"key":"10.1016\/j.neucom.2026.134587_bib0110","series-title":"The Eleventh International Conference on Learning Representations","article-title":"Decompositional generation process for instance-dependent partial label learning","author":"Qiao","year":"2023"},{"key":"10.1016\/j.neucom.2026.134587_bib0115","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1016\/j.neunet.2023.02.019","article-title":"Partial label learning: taxonomy, analysis and outlook","volume":"161","author":"Tian","year":"2023","journal-title":"Neural Netw."},{"key":"10.1016\/j.neucom.2026.134587_bib0120","author":"Ye"},{"issue":"4","key":"10.1016\/j.neucom.2026.134587_bib0125","doi-asserted-by":"crossref","first-page":"1906","DOI":"10.1109\/TAFFC.2024.3385651","article-title":"FBSTCNet: a spatio-temporal convolutional network integrating power and connectivity features for EEG-based emotion decoding","volume":"15","author":"Huang","year":"2024","journal-title":"IEEE Trans. Affect. Comput."},{"key":"10.1016\/j.neucom.2026.134587_bib0130","series-title":"ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"2325","article-title":"A supervised information enhanced multi-granularity contrastive learning framework for EEG based emotion recognition","author":"Li","year":"2024"},{"key":"10.1016\/j.neucom.2026.134587_bib0135","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"628","article-title":"DMMR: cross-subject domain generalization for EEG-based emotion recognition via denoising mixed mutual reconstruction","volume":"vol. 38","author":"Wang","year":"2024"},{"issue":"2519911","key":"10.1016\/j.neucom.2026.134587_bib0140","article-title":"Generalized contrastive partial label learning for cross-subject EEG-based emotion recognition","volume":"73","author":"Li","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.neucom.2026.134587_bib0145","series-title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","first-page":"1532","article-title":"GloVe: global vectors for word representation","author":"Pennington","year":"2014"},{"key":"10.1016\/j.neucom.2026.134587_bib0150","series-title":"Affective Norms for English Words (ANEW): Instruction Manual and Affective Ratings","author":"Bradley","year":"1999"},{"key":"10.1016\/j.neucom.2026.134587_bib0155","series-title":"International Conference on Learning Representations","article-title":"mixup: beyond empirical risk minimization","author":"Zhang","year":"2018"},{"key":"10.1016\/j.neucom.2026.134587_bib0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.130418","article-title":"Emotion recognition via affective EEG signals: state of the art","volume":"643","author":"Meng","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134587_bib0165","series-title":"International Conference on Neural Information Processing","first-page":"186","article-title":"Two-stream spectral-temporal denoising network for end-to-end robust EEG-based emotion recognition","volume":"vol. 14449","author":"Liu","year":"2023"},{"key":"10.1016\/j.neucom.2026.134587_bib0170","series-title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","first-page":"3078","article-title":"VSGT: variational spatial and Gaussian temporal graph models for EEG-based emotion recognition","author":"Liu","year":"2024"},{"key":"10.1016\/j.neucom.2026.134587_bib0175","doi-asserted-by":"crossref","first-page":"9070","DOI":"10.1109\/TMM.2024.3385676","article-title":"PGCN: pyramidal graph convolutional network for EEG emotion recognition","volume":"26","author":"Jin","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.neucom.2026.134587_bib0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.131822","article-title":"A hybrid SGC-transformer network for EEG emotion recognition with historical data integration","volume":"659","author":"Yang","year":"2026","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134587_bib0185","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"863","article-title":"Plug-and-play domain adaptation for cross-subject EEG-based emotion recognition","volume":"vol. 35","author":"Zhao","year":"2021"},{"key":"10.1016\/j.neucom.2026.134587_bib0190","series-title":"2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","first-page":"3515","article-title":"MoGE: mixture of graph experts for cross-subject emotion recognition via decomposing EEG","author":"Liu","year":"2024"},{"key":"10.1016\/j.neucom.2026.134587_bib0195","first-page":"1","article-title":"Enhancing EEG-based cross-subject emotion recognition via adaptive source joint domain adaptation","author":"Liu","year":"2024","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"4","key":"10.1016\/j.neucom.2026.134587_bib0200","doi-asserted-by":"crossref","first-page":"1970","DOI":"10.1109\/TAFFC.2024.3392791","article-title":"CiABL: completeness-induced adaptative broad learning for cross-subject emotion recognition with EEG and EYE movement signals","volume":"15","author":"Gong","year":"2024","journal-title":"IEEE Trans. Affect. Comput."},{"key":"10.1016\/j.neucom.2026.134587_bib0205","first-page":"1","article-title":"From EEG to eye movements: cross-modal emotion recognition using constrained adversarial network with dual attention","author":"Wang","year":"2024","journal-title":"IEEE Trans. Affect. Comput."},{"key":"10.1016\/j.neucom.2026.134587_bib0210","series-title":"IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"1","article-title":"Deeply coupling EEG signals and eye movements for multi-modal and region-aware emotion recognition","author":"Chen","year":"2025"},{"key":"10.1016\/j.neucom.2026.134587_bib0215","series-title":"The Twelfth International Conference on Learning Representations","article-title":"Large brain model for learning generic representations with tremendous EEG data in BCI","author":"Jiang","year":"2024"},{"key":"10.1016\/j.neucom.2026.134587_bib0220","series-title":"The Thirteenth International Conference on Learning Representations","article-title":"NeuroLM: a universal multi-task foundation model for bridging the gap between language and EEG signals","author":"Jiang","year":"2025"},{"key":"10.1016\/j.neucom.2026.134587_bib0225","series-title":"The Thirty-Ninth Annual Conference on Neural Information Processing Systems","article-title":"REVE: a foundation model for EEG \u2013 adapting to any setup with large-scale pretraining on 25, 000 subjects","author":"Ouahidi","year":"2025"},{"key":"10.1016\/j.neucom.2026.134587_bib0230","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"17427","article-title":"Emod: a unified EEG emotion representation framework leveraging VA guided contrastive learning","volume":"vol. 40","author":"Chen","year":"2026"},{"issue":"5","key":"10.1016\/j.neucom.2026.134587_bib0235","doi-asserted-by":"crossref","first-page":"2569","DOI":"10.1109\/TPAMI.2023.3275249","article-title":"On the robustness of average losses for partial-label learning","volume":"46","author":"Lv","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2026.134587_bib0240","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"15888","article-title":"Distilling reliable knowledge for instance-dependent partial label learning","volume":"vol. 38","author":"Wu","year":"2024"},{"key":"10.1016\/j.neucom.2026.134587_bib0245","series-title":"ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"3820","article-title":"On the power of deep but naive partial label learning","author":"Seo","year":"2021"},{"key":"10.1016\/j.neucom.2026.134587_bib0250","series-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)","first-page":"4171","article-title":"BERT: pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2019"},{"issue":"4","key":"10.1016\/j.neucom.2026.134587_bib0255","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1007\/BF02288916","article-title":"Multidimensional scaling: I. theory and method","volume":"17","author":"Torgerson","year":"1952","journal-title":"Psychometrika"},{"key":"10.1016\/j.neucom.2026.134587_bib0260","series-title":"2013 6th International IEEE\/EMBS Conference on Neural Engineering (NER)","first-page":"81","article-title":"Differential entropy feature for EEG-based emotion classification","author":"Duan","year":"2013"},{"key":"10.1016\/j.neucom.2026.134587_bib0265","series-title":"Advances in Neural Information Processing Systems","first-page":"4080","article-title":"Prototypical networks for few-shot learning","volume":"vol. 30","author":"Snell","year":"2017"},{"key":"10.1016\/j.neucom.2026.134587_bib0270","series-title":"2021 IEEE International Conference on Multimedia and Expo (ICME)","first-page":"1","article-title":"A generative model for partial label learning","author":"Yan","year":"2021"},{"key":"10.1016\/j.neucom.2026.134587_bib0275","series-title":"International Conference on Machine Learning","first-page":"24212","article-title":"Revisiting consistency regularization for deep partial label learning","volume":"vol. 162","author":"Wu","year":"2022"},{"issue":"4","key":"10.1016\/j.neucom.2026.134587_bib0280","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3524499","article-title":"EEG based emotion recognition: a tutorial and review","volume":"55","author":"Li","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.neucom.2026.134587_bib0285","series-title":"International Conference on Neural Information Processing","first-page":"403","article-title":"Cross-subject emotion recognition using deep adaptation networks","volume":"vol. 11305","author":"Li","year":"2018"},{"issue":"778488","key":"10.1016\/j.neucom.2026.134587_bib0290","article-title":"MS-MDA: multisource marginal distribution adaptation for cross-subject and cross-session EEG emotion recognition","volume":"15","author":"Chen","year":"2021","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.neucom.2026.134587_bib0295","series-title":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","first-page":"5962","article-title":"Reducing the calibration effort of EEG emotion recognition using domain adaptation with soft labels","author":"Li","year":"2021"},{"issue":"2","key":"10.1016\/j.neucom.2026.134587_bib0300","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1109\/TAFFC.2023.3288118","article-title":"PR-PL: a novel prototypical representation based pairwise learning framework for emotion recognition using EEG signals","volume":"15","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"3","key":"10.1016\/j.neucom.2026.134587_bib0305","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1109\/TAFFC.2018.2817622","article-title":"EEG emotion recognition using dynamical graph convolutional neural networks","volume":"11","author":"Song","year":"2018","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"2","key":"10.1016\/j.neucom.2026.134587_bib0310","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1109\/TCDS.2021.3071170","article-title":"Comparing recognition performance and robustness of multimodal deep learning models for multimodal emotion recognition","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"issue":"1\u201316","key":"10.1016\/j.neucom.2026.134587_bib0315","article-title":"SEED-VII: a multimodal dataset of six basic emotions with continuous labels for emotion recognition","author":"Jiang","year":"2024","journal-title":"IEEE Trans. Affect. Comput."},{"key":"10.1016\/j.neucom.2026.134587_bib0320","series-title":"International Conference on Learning Representations","article-title":"Exploiting class activation value for partial-label learning","author":"Zhang","year":"2021"},{"key":"10.1016\/j.neucom.2026.134587_bib0325","series-title":"International Conference on Machine Learning","first-page":"6500","article-title":"Progressive identification of true labels for partial-label learning","volume":"vol. 119","author":"Lv","year":"2020"},{"key":"10.1016\/j.neucom.2026.134587_bib0330","series-title":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","first-page":"1763","article-title":"Mixed blessing: class-wise embedding guided instance-dependent partial label learning","author":"Yang","year":"2025"},{"key":"10.1016\/j.neucom.2026.134587_bib0335","series-title":"ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"1","article-title":"Elastic graph transformer networks for EEG-based emotion recognition","author":"Jiang","year":"2023"},{"key":"10.1016\/j.neucom.2026.134587_bib0340","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128354","article-title":"Toward cross-subject and cross-session generalization in EEG-based emotion recognition: systematic review, taxonomy, and methods","volume":"604","author":"Apicella","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134587_bib0345","series-title":"Conference on Learning Theory (COLT)","first-page":"1376","article-title":"Norm-based capacity control in neural networks","volume":"vol. 40","author":"Neyshabur","year":"2015"},{"key":"10.1016\/j.neucom.2026.134587_bib0350","first-page":"463","article-title":"Rademacher and Gaussian complexities: risk bounds and structural results","volume":"3","author":"Bartlett","year":"2002","journal-title":"J. Mach. Learn. Res."},{"issue":"8","key":"10.1016\/j.neucom.2026.134587_bib0355","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation learning: a review and new perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2026.134587_bib0360","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1007\/s10994-020-05929-w","article-title":"Regularisation of neural networks by enforcing Lipschitz continuity","volume":"110","author":"Gouk","year":"2021","journal-title":"Mach. Learn."},{"key":"10.1016\/j.neucom.2026.134587_bib0365","series-title":"Understanding Machine Learning: From Theory to Algorithms","author":"Shalev-Shwartz","year":"2014"},{"key":"10.1016\/j.neucom.2026.134587_bib0370","series-title":"Proceedings of the International Conference on Neural Information Processing","first-page":"30","article-title":"Reducing the subject variability of EEG signals with adversarial domain generalization","author":"Ma","year":"2019"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226019855?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226019855?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T05:42:22Z","timestamp":1786081342000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226019855"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":74,"alternative-id":["S0925231226019855"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.134587","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Learning from semantic ambiguity: A dual-noise robust framework for EEG partial label emotion recognition","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.134587","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"134587"}}