{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T15:47:25Z","timestamp":1773244045776,"version":"3.50.1"},"reference-count":38,"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"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.bspc.2026.109504","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T20:41:22Z","timestamp":1769546482000},"page":"109504","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Hybrid classification with ensemble representation learning for subject-independent EEG-based motor imagery"],"prefix":"10.1016","volume":"118","author":[{"given":"Hamidreza","family":"Hosseinzadeh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2026.109504_b0005","series-title":"Brain-computer interfaces: Principles and practice","author":"Wolpaw","year":"2012"},{"issue":"8","key":"10.1016\/j.bspc.2026.109504_b0010","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1016\/j.tics.2021.04.003","article-title":"Interface, interaction, and intelligence in generalized brain-computer interfaces","volume":"25","author":"Gao","year":"2021","journal-title":"Trends Cogn. Sci."},{"key":"10.1016\/j.bspc.2026.109504_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105293","article-title":"MI brain-computer interfaces: A concise overview","volume":"86","author":"Mandal","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.109504_b0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2019.103455","article-title":"Real-time EEG classification via coresets for BCI applications","volume":"89","author":"Netzer","year":"2020","journal-title":"Eng. Appl. Artif. Intel."},{"key":"10.1016\/j.bspc.2026.109504_b0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105556","article-title":"Manifold embedded instance selection to suppress negative transfer in motor imagery-based brain\u2013computer interface","volume":"88","author":"Liang","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.109504_b0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103843","article-title":"Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application","volume":"123","author":"Khan","year":"2020","journal-title":"Comput. Biol. Med."},{"issue":"10","key":"10.1016\/j.bspc.2026.109504_b0035","doi-asserted-by":"crossref","first-page":"1554","DOI":"10.1038\/s41593-019-0488-y","article-title":"Brain-machine interfaces from motor to mood","volume":"22","author":"Shanechi","year":"2019","journal-title":"Nat. Neurosci."},{"key":"10.1016\/j.bspc.2026.109504_b0040","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.neunet.2022.06.008","article-title":"Transfer learning for motor imagery based brain-computer interfaces: A tutorial","volume":"153","author":"Wu","year":"2022","journal-title":"Neural Netw."},{"issue":"1","key":"10.1016\/j.bspc.2026.109504_b0045","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TCDS.2020.3007453","article-title":"Transfer learning for EEG-based brain-computer interfaces: A review of progress made since 2016","volume":"14","author":"Wu","year":"2020","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"issue":"7","key":"10.1016\/j.bspc.2026.109504_b0050","doi-asserted-by":"crossref","first-page":"1352","DOI":"10.1109\/TNSRE.2019.2923315","article-title":"Weighted transfer learning for improving motor imagery-based brain-computer interface","volume":"27","author":"Azab","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.bspc.2026.109504_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.105347","article-title":"Adaptive transfer learning-based multiscale feature fused deep convolutional neural network for EEG MI multiclassification in brain\u2013computer interface","volume":"116","author":"Roy","year":"2022","journal-title":"Eng. Appl. Artif. Intel."},{"issue":"2","key":"10.1016\/j.bspc.2026.109504_b0060","first-page":"399","article-title":"Transfer learning for brain\u2013computer interfaces: A Euclidean space data alignment approach","volume":"67","author":"He","year":"2020","journal-title":"I.E.E.E. Trans. Biomed. Eng."},{"key":"10.1016\/j.bspc.2026.109504_b0065","doi-asserted-by":"crossref","first-page":"1258","DOI":"10.1109\/TNSRE.2023.3243257","article-title":"A multi-source transfer joint matching method for inter-subject motor imagery decoding","volume":"31","author":"Wei","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"5","key":"10.1016\/j.bspc.2026.109504_b0070","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1109\/TNSRE.2020.2985996","article-title":"Manifold embedded knowledge transfer for brain-computer interfaces","volume":"28","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.bspc.2026.109504_b0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.jneumeth.2021.109378","article-title":"Multi-view optimization of time-frequency common spatial patterns for brain-computer interfaces","volume":"365","author":"Huang","year":"2022","journal-title":"J. Neurosci. Methods"},{"key":"10.1016\/j.bspc.2026.109504_b0080","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.neucom.2022.09.124","article-title":"Multi-source manifold feature transfer learning with domain selection for brain-computer interfaces","volume":"514","author":"She","year":"2022","journal-title":"Neurocomputing"},{"issue":"19","key":"10.1016\/j.bspc.2026.109504_b0085","doi-asserted-by":"crossref","first-page":"21772","DOI":"10.1109\/JSEN.2021.3101684","article-title":"Multi-source fusion domain adaptation using resting-state knowledge for motor imagery classification tasks","volume":"21","author":"Zhu","year":"2021","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.bspc.2026.109504_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.jneumeth.2022.109489","article-title":"Motor imagery EEG decoding using manifold embedded transfer learning","volume":"370","author":"Cai","year":"2022","journal-title":"J. Neurosci. Methods"},{"issue":"1","key":"10.1016\/j.bspc.2026.109504_b0095","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1109\/JBHI.2022.3218453","article-title":"Domain adaptive algorithm based on multi-manifold embedded distributed alignment for brain-computer interfaces","volume":"27","author":"Gao","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.bspc.2026.109504_b0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106311","article-title":"A novel deep transfer learning framework integrating general and domain-specific features for EEG-based brain\u2013computer interface","volume":"95","author":"Liang","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.109504_b0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107254","article-title":"A shallow mirror transformer for subject-independent motor imagery BCI","volume":"164","author":"Luo","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.109504_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105786","article-title":"A deep transfer learning network with two classifiers based on sample selection for motor imagery brain-computer interface","volume":"89","author":"Zheng","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.109504_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106797","article-title":"Enhancing motor imagery decoding in brain\u2013computer interfaces using Riemann tangent space mapping and cross frequency coupling","volume":"99","author":"Xiong","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.109504_b0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113074","article-title":"Time-frequency transform based EEG data augmentation for brain-computer interfaces","volume":"311","author":"Wang","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.bspc.2026.109504_b0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.110015","article-title":"GITGAN: Generative inter-subject transfer for EEG motor imagery analysis","volume":"146","author":"Yin","year":"2024","journal-title":"Pattern Recogn."},{"issue":"2","key":"10.1016\/j.bspc.2026.109504_b0130","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.neuroimage.2007.01.051","article-title":"The non-invasive Berlin brain\u2013computer interface: Fast acquisition of effective performance in untrained subjects","volume":"37","author":"Blankertz","year":"2007","journal-title":"Neuroimage"},{"key":"10.1016\/j.bspc.2026.109504_b0135","series-title":"BCI competition 2008\u2014Graz data set a, Inst","first-page":"1","author":"Brunner","year":"2008"},{"issue":"5","key":"10.1016\/j.bspc.2026.109504_b0140","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1109\/TNSRE.2020.2980299","article-title":"Different set domain adaptation for brain-computer interfaces: A label alignment approach","volume":"28","author":"He","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.bspc.2026.109504_b0145","series-title":"A panoramic view of riemannian geometry","author":"Berger","year":"2003"},{"key":"10.1016\/j.bspc.2026.109504_b0150","doi-asserted-by":"crossref","first-page":"4402","DOI":"10.1109\/TNSRE.2023.3329482","article-title":"Attentional state classification using amplitude and phase feature extraction method based on filter bank and riemannian manifold","volume":"31","author":"Xu","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"5","key":"10.1016\/j.bspc.2026.109504_b0155","first-page":"1107","article-title":"Transfer learning: A Riemannian geometry framework with applications to brain\u2013computer interfaces","volume":"65","author":"Zanini","year":"2018","journal-title":"I.E.E.E. Trans. Biomed. Eng."},{"key":"10.1016\/j.bspc.2026.109504_b0160","doi-asserted-by":"crossref","first-page":"139457","DOI":"10.1109\/ACCESS.2023.3340685","article-title":"Common spatial pattern and Riemannian manifold based real-time multiclass motor imagery EEG classification","volume":"11","author":"Shyu","year":"2023","journal-title":"IEEE Access"},{"issue":"1\u20132","key":"10.1016\/j.bspc.2026.109504_b0165","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-009-9124-7","article-title":"Ensemble-based classifiers","volume":"33","author":"Rokach","year":"2010","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.bspc.2026.109504_b0170","series-title":"In Proceedings of the IEEE International Conference on Computer Vision","first-page":"2072","article-title":"Van Gool, Ensemble projection for semi-supervised image classification","author":"Dai","year":"2013"},{"key":"10.1016\/j.bspc.2026.109504_b0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2021.109346","article-title":"A weakly supervised representation learning for modulation recognition of short duration signals","volume":"178","author":"Hosseinzadeh","year":"2021","journal-title":"Measurement"},{"issue":"2","key":"10.1016\/j.bspc.2026.109504_b0180","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A tutorial on support vector machines for pattern recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Disc."},{"key":"10.1016\/j.bspc.2026.109504_b0185","unstructured":"K. Koutroumbas, S. Theodoridis, Pattern Recognition, New York, NY, USA:Academic, 2008."},{"key":"10.1016\/j.bspc.2026.109504_b0190","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426000583?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426000583?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T20:22:10Z","timestamp":1773174130000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426000583"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":38,"alternative-id":["S1746809426000583"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.109504","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Hybrid classification with ensemble representation learning for subject-independent EEG-based motor imagery","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.109504","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"109504"}}