{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T21:36:36Z","timestamp":1780695396401,"version":"3.54.1"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T00:00:00Z","timestamp":1707696000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T00:00:00Z","timestamp":1707696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LZ22F010003"],"award-info":[{"award-number":["LZ22F010003"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62371172"],"award-info":[{"award-number":["62371172"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271181"],"award-info":[{"award-number":["62271181"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s11517-024-03036-9","type":"journal-article","created":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T03:04:08Z","timestamp":1707707048000},"page":"1689-1701","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Three-stage transfer learning for motor imagery EEG recognition"],"prefix":"10.1007","volume":"62","author":[{"given":"Junhao","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5206-9833","authenticated-orcid":false,"given":"Qingshan","family":"She","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengzhi","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingchun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,12]]},"reference":[{"issue":"6","key":"3036_CR1","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1109\/JPROC.2015.2407272","volume":"103","author":"B He","year":"2015","unstructured":"He B, Baxter B, Edelman BJ (2015) Noninvasive brain-computer interfaces based on sensorimotor rhythms. Proc IEEE Inst Electr Electron Eng 103(6):907\u2013925. https:\/\/doi.org\/10.1109\/JPROC.2015.2407272","journal-title":"Proc IEEE Inst Electr Electron Eng"},{"issue":"4","key":"3036_CR2","doi-asserted-by":"publisher","first-page":"041001","DOI":"10.1109\/JPROC.2015.2407272","volume":"17","author":"R Mane","year":"2020","unstructured":"Mane R, Chouhan T, Guan C (2020) BCI for stroke rehabilitation: motor and beyond. J Neural Eng 17(4):041001. https:\/\/doi.org\/10.1109\/JPROC.2015.2407272","journal-title":"J Neural Eng"},{"issue":"3","key":"3036_CR3","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1109\/TBME.2019.2921198","volume":"67","author":"R Foong","year":"2020","unstructured":"Foong R, Ang KK, Quek C, Guan C, Phua KS, Kuah CWK, Deshmukh VA, Yam LHL, Rajeswaran DK, Tang N, Chew E, Chua KSG (2020) Assessment of the efficacy of EEG-based MI-BCI with visual feedback and EEG correlates of mental fatigue for upper-limb stroke rehabilitation. IEEE Trans Biomed Eng 67(3):786\u2013795. https:\/\/doi.org\/10.1109\/TBME.2019.2921198","journal-title":"IEEE Trans Biomed Eng"},{"issue":"6","key":"3036_CR4","doi-asserted-by":"publisher","first-page":"061003","DOI":"10.1088\/1741-2552\/ac36aa","volume":"18","author":"MA Khan","year":"2012","unstructured":"Khan MA, Saibene M, Das R, Brunner I, Puthusserypady S (2012) Emergence of flexible technology in developing advanced systems for post-stroke rehabilitation: a comprehensive review. J Neural Eng 18(6):061003. https:\/\/doi.org\/10.1088\/1741-2552\/ac36aa","journal-title":"J Neural Eng"},{"key":"3036_CR5","doi-asserted-by":"publisher","unstructured":"Lee J, Lee D, Jeong I, Cho. J (2021) A Study on the content of mental and physical stability game in virtual reality through EEG detection. In: 2021 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea, pp 693\u2013696. https:\/\/doi.org\/10.1109\/ICTC52510.2021.9620932","DOI":"10.1109\/ICTC52510.2021.9620932"},{"key":"3036_CR6","doi-asserted-by":"publisher","first-page":"9967348","DOI":"10.1155\/2021\/9967348","volume":"2021","author":"S Yang","year":"2021","unstructured":"Yang S, Li R, Li H, Xu K, Shi Y, Wang Q, Yang T, Sun X (2021) Exploring the use of brain-computer interfaces in stroke neurorehabilitation. BioMed Res Int 2021:9967348. https:\/\/doi.org\/10.1155\/2021\/9967348","journal-title":"BioMed Res Int"},{"issue":"2","key":"3036_CR7","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1109\/TNSRE.2018.2790359","volume":"26","author":"CS Wei","year":"2018","unstructured":"Wei CS, Wang YT, Lin CT, Jung TP (2018) Toward drowsiness detection using non-hair-bearing EEG-based brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng 26(2):400\u2013406. https:\/\/doi.org\/10.1109\/TNSRE.2018.2790359","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"7","key":"3036_CR8","doi-asserted-by":"publisher","first-page":"2313","DOI":"10.1109\/TBME.2021.3069119","volume":"68","author":"JD Simeral","year":"2021","unstructured":"Simeral JD, Hosman T, Saab J et al (2021) Home use of a percutaneous wireless intracortical brain-computer interface by individuals with tetraplegia. IEEE Trans Biomed Eng 68(7):2313\u20132325. https:\/\/doi.org\/10.1109\/TBME.2021.3069119","journal-title":"IEEE Trans Biomed Eng"},{"issue":"3","key":"3036_CR9","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1109\/TBME.2022.3204718","volume":"70","author":"Y Gao","year":"2022","unstructured":"Gao Y, Liu H, Fang F, Zhang Y (2022) Classification of working memory loads via assessing broken detailed balance of EEG-fNIRS neurovascular coupling measures. IEEE Trans Biomed Eng 70(3):877\u2013887. https:\/\/doi.org\/10.1109\/TBME.2022.3204718","journal-title":"IEEE Trans Biomed Eng"},{"issue":"12","key":"3036_CR10","doi-asserted-by":"publisher","first-page":"3051","DOI":"10.1109\/TNSRE.2020.3039331","volume":"28","author":"VK Benzy","year":"2020","unstructured":"Benzy VK, Vinod AP, Subasree R, Alladi S, Raghavendra K (2020) Motor imagery hand Movement direction decoding using brain computer interface to aid stroke recovery and Rehabilitation. IEEE Trans Neural Syst Rehabil Eng 28(12):3051\u20133062. https:\/\/doi.org\/10.1109\/TNSRE.2020.3039331","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"3036_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2020.09.017","volume":"421","author":"Z Wang","year":"2021","unstructured":"Wang Z, Yang R, Huang M, Zeng N, Liu X (2021) A review on transfer learning in EEG signal analysis. Neurocomputing 421:1\u201314. https:\/\/doi.org\/10.1016\/j.neucom.2020.09.017","journal-title":"Neurocomputing"},{"issue":"8","key":"3036_CR12","doi-asserted-by":"publisher","first-page":"2390","DOI":"10.1109\/TBME.2018.2889705","volume":"66","author":"PLC Rodrigues","year":"2019","unstructured":"Rodrigues PLC, Jutten C, Congedo M (2019) Riemannian procrustes analysis: transfer learning for brain-computer interfaces. IEEE Trans Biomed Eng 66(8):2390\u20132401. https:\/\/doi.org\/10.1109\/TBME.2018.2889705","journal-title":"IEEE Trans Biomed Eng"},{"issue":"8","key":"3036_CR13","doi-asserted-by":"publisher","first-page":"2289","DOI":"10.1109\/TBME.2013.2253608","volume":"60","author":"W Samek","year":"2013","unstructured":"Samek W, Meinecke FC, M\u00fcller KR (2013) Transferring subspaces between subjects in brain\u2013computer interfacing. IEEE Trans Biomed Eng 60(8):2289\u20132298. https:\/\/doi.org\/10.1109\/TBME.2013.2253608","journal-title":"IEEE Trans Biomed Eng"},{"issue":"3","key":"3036_CR14","doi-asserted-by":"publisher","first-page":"031005","DOI":"10.1088\/1741-2552\/aab2f2","volume":"15","author":"F Lotte","year":"2018","unstructured":"Lotte F, Bougrain L, Cichocki A, Clerc M, Congedo M, Rakotomamonjy A, Yger F (2018) A review of classification algorithms for EEG-based brain\u2013computer interfaces: a 10 year update. J Neural Eng 15(3):031005. https:\/\/doi.org\/10.1088\/1741-2552\/aab2f2","journal-title":"J Neural Eng"},{"issue":"4","key":"3036_CR15","doi-asserted-by":"publisher","first-page":"045009","DOI":"10.1088\/1741-2560\/9\/4\/045009","volume":"9","author":"O Falzon","year":"2012","unstructured":"Falzon O, Camilleri KP, Muscat J (2012) The analytic common spatial patterns method for EEG-based BCI data. J Neural Eng 9(4):045009. https:\/\/doi.org\/10.1088\/1741-2560\/9\/4\/045009","journal-title":"J Neural Eng"},{"issue":"3","key":"3036_CR16","doi-asserted-by":"publisher","first-page":"031002","DOI":"10.1088\/1741-2552\/ac74e0","volume":"19","author":"P Arpaia","year":"2022","unstructured":"Arpaia P, Esposito A, Natalizio A, Marco P (2022) How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art. J Neural Eng 19(3):031002. https:\/\/doi.org\/10.1088\/1741-2552\/ac74e0","journal-title":"J Neural Eng"},{"key":"3036_CR17","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.eswa.2016.08.044","volume":"65","author":"A Atyabi","year":"2016","unstructured":"Atyabi A, Shic F, Naples A (2016) Mixture of autoregressive modeling orders and its implication on single trial EEG classification. Expert Syst Appl 65:164\u2013180. https:\/\/doi.org\/10.1016\/j.eswa.2016.08.044","journal-title":"Expert Syst Appl"},{"issue":"3","key":"3036_CR18","doi-asserted-by":"publisher","first-page":"031001","DOI":"10.1088\/1741-2552\/ab0ab5","volume":"16","author":"A Craik","year":"2019","unstructured":"Craik A, He Y, Contreras-Vidal JL (2019) Deep learning for electroencephalogram (EEG) classification tasks: a review. J Neural Eng 16(3):031001. https:\/\/doi.org\/10.1088\/1741-2552\/ab0ab5","journal-title":"J Neural Eng"},{"key":"3036_CR19","doi-asserted-by":"publisher","first-page":"109736","DOI":"10.1016\/j.jneumeth.2022.109736","volume":"383","author":"Z Khademi","year":"2023","unstructured":"Khademi Z, Ebrahimi F, Kordy HM (2023) A review of critical challenges in MI-BCI: From conventional to deep learning methods. J Neurosci Methods 383:109736. https:\/\/doi.org\/10.1016\/j.jneumeth.2022.109736","journal-title":"J Neurosci Methods"},{"key":"3036_CR20","doi-asserted-by":"publisher","first-page":"2866","DOI":"10.1109\/TNSRE.2022.3211881","volume":"30","author":"P Chen","year":"2022","unstructured":"Chen P, Wang H, Sun X, Li H, Grebogi C, Gao Z (2022) Transfer learning with optimal transportation and frequency mixup for EEG-based motor imagery recognition. IEEE Trans Neural Syst Rehabil Eng 30:2866\u20132875. https:\/\/doi.org\/10.1109\/TNSRE.2022.3211881","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"10","key":"3036_CR21","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359. https:\/\/doi.org\/10.1109\/TKDE.2009.191","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"3036_CR22","doi-asserted-by":"publisher","first-page":"7100704","DOI":"10.1109\/LSENS.2018.2879466","volume":"3","author":"E Dagois","year":"2019","unstructured":"Dagois E, Khalaf A, Sejdic E, Akcakaya M (2019) Transfer learning for a multimodal hybrid EEG-fTCD brain\u2013computer interface. IEEE Sens Lett 3(1):7100704. https:\/\/doi.org\/10.1109\/LSENS.2018.2879466","journal-title":"IEEE Sens Lett"},{"issue":"10","key":"3036_CR23","doi-asserted-by":"publisher","first-page":"1950025","DOI":"10.1142\/S0129065719500254","volume":"29","author":"P Gaur","year":"2019","unstructured":"Gaur P, McCreadie K, Pachori RB (2019) Tangent space features-based transfer learning classification model for two-class motor imagery brain\u2013computer interface. Int J Neural Syst 29(10):1950025. https:\/\/doi.org\/10.1142\/S0129065719500254","journal-title":"Int J Neural Syst"},{"issue":"1","key":"3036_CR24","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2021","unstructured":"Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, Xiong H, He Q (2021) A comprehensive survey on transfer learning. Proc IEEE Inst Electr Electron Eng 109(1):43\u201376. https:\/\/doi.org\/10.1109\/JPROC.2020.3004555","journal-title":"Proc IEEE Inst Electr Electron Eng"},{"issue":"2","key":"3036_CR25","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1109\/TBME.2019.2913914","volume":"67","author":"H He","year":"2020","unstructured":"He H, Wu D (2020) Transfer learning for brain\u2013computer interfaces: a Euclidean space data alignment approach. IEEE Trans Biomed Eng 67(2):399\u2013410. https:\/\/doi.org\/10.1109\/TBME.2019.2913914","journal-title":"IEEE Trans Biomed Eng"},{"issue":"5","key":"3036_CR26","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1109\/TBME.2017.2742541","volume":"65","author":"P Zanini","year":"2017","unstructured":"Zanini P, Congedo M, Jutten C, Said S, Berthoumieu Y (2017) Transfer learning: a Riemannian geometry framework with applications to brain-computer interfaces. IEEE Trans Biomed Eng 65(5):1107\u20131116. https:\/\/doi.org\/10.1109\/TBME.2017.2742541","journal-title":"IEEE Trans Biomed Eng"},{"key":"3036_CR27","doi-asserted-by":"publisher","unstructured":"Wang J, Chen Y, Hao S, Feng W, Shen Z (2017) Balanced distribution adaptation for transfer learning. In: 2017 IEEE Int Conf Data Mining (ICDM), New Orleans, LA, USA, pp 1129\u20131134. https:\/\/doi.org\/10.1109\/ICDM.2017.150","DOI":"10.1109\/ICDM.2017.150"},{"issue":"5","key":"3036_CR28","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.1109\/TNSRE.2020.2985996","volume":"28","author":"W Zhang","year":"2020","unstructured":"Zhang W, Wu D (2020) Manifold embedded knowledge transfer for brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng 28(5):1117\u20131127. https:\/\/doi.org\/10.1109\/TNSRE.2020.2985996","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"2","key":"3036_CR29","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","volume":"22","author":"SJ Pan","year":"2011","unstructured":"Pan SJ, Tsang IW, Kwok JT, Yang Q (2011) Domain adaptation via transfer component analysis. I IEEE Trans Neural Netw 22(2):199\u2013210. https:\/\/doi.org\/10.1109\/TNN.2010.2091281","journal-title":"I IEEE Trans Neural Netw"},{"key":"3036_CR30","doi-asserted-by":"publisher","first-page":"109489","DOI":"10.1016\/j.jneumeth.2022.109489","volume":"370","author":"Y Cai","year":"2022","unstructured":"Cai Y, She Q, Ji J, Ma Y, Zhang J, Zhang Y (2022) Motor imagery EEG decoding using manifold embedded transfer learning. J Neurosci Methods 370:109489. https:\/\/doi.org\/10.1016\/j.jneumeth.2022.109489","journal-title":"J Neurosci Methods"},{"issue":"1","key":"3036_CR31","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1109\/JBHI.2022.3218453","volume":"27","author":"Y Gao","year":"2022","unstructured":"Gao Y, Liu Y, She Q, Zhang J (2022) Domain adaptive algorithm based on multi-manifold embedded distributed alignment for brain-computer interfaces. IEEE J Biomed Health Informat 27(1):296\u2013307. https:\/\/doi.org\/10.1109\/JBHI.2022.3218453","journal-title":"IEEE J Biomed Health Informat"},{"key":"3036_CR32","unstructured":"Nguyen T, Le T, Zhao H, Tran QH, Nguyen T, Phung D (2021) Most: multi-source domain adaptation via optimal transport for student-teacher learning. In: Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence (UAI). 161:225\u2013235. https:\/\/www.auai.org\/uai2021\/pdf\/uai2021.106.preliminary.pdf. Accessed 9 Feb 2024"},{"key":"3036_CR33","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.neucom.2022.09.124","volume":"514","author":"Q She","year":"2022","unstructured":"She Q, Cai Y, Du S, Chen Y (2022) Multi-source manifold feature transfer learning with domain selection for brain-computer interfaces. Neurocomputing 514:313\u2013327. https:\/\/doi.org\/10.1016\/j.neucom.2022.09.124","journal-title":"Neurocomputing"},{"issue":"9","key":"3036_CR34","doi-asserted-by":"publisher","first-page":"1853","DOI":"10.1109\/TPAMI.2016.2615921","volume":"39","author":"N Courty","year":"2017","unstructured":"Courty N, Flamary R, Tuia D, Rakotomamonjy A (2017) Optimal transport for domain adaptation. IEEE Trans Pattern Anal Mach Intell 39(9):1853\u20131865. https:\/\/doi.org\/10.1109\/TPAMI.2016.2615921","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"3036_CR35","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1109\/TBME.2021.3105912","volume":"69","author":"V Peterson","year":"2021","unstructured":"Peterson V, Nieto N, Wyser D, Lambercy O, Gassert R, H. Milone D, D. Spies R, (2021) Transfer learning based on optimal transport for motor imagery brain-computer interfaces. IEEE Trans Biomed Eng 69(2):807\u2013817. https:\/\/doi.org\/10.1109\/TBME.2021.3105912","journal-title":"IEEE Trans Biomed Eng"},{"issue":"2","key":"3036_CR36","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/j.neuroimage.2007.01.051","volume":"37","author":"B Blankertz","year":"2007","unstructured":"Blankertz B, Dornhege G, Krauledat M, M\u00fcller KR, Curio G (2007) The non-invasive Berlin brain-computer interface: fast acquisition of effective performance in untrained subjects. Neuroimage 37(2):539\u2013550. https:\/\/doi.org\/10.1016\/j.neuroimage.2007.01.051","journal-title":"Neuroimage"},{"issue":"3","key":"3036_CR37","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1088\/1741-2560\/3\/3\/003","volume":"3","author":"M Naeem","year":"2006","unstructured":"Naeem M, Brunner C, Leeb R, Graimann B, Pfurtscheller G (2006) Seperability of four-class motor imagery data using independent components analysis. J Neural Eng 3(3):208\u2013216. https:\/\/doi.org\/10.1088\/1741-2560\/3\/3\/003","journal-title":"J Neural Eng"},{"issue":"4","key":"3036_CR38","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.1287\/mnsc.5.1.1","volume":"133","author":"LV Kantorovich","year":"2006","unstructured":"Kantorovich LV (2006) On the translocation of masses. J Math Sci 133(4):1381\u20131382. https:\/\/doi.org\/10.1287\/mnsc.5.1.1","journal-title":"J Math Sci"},{"key":"3036_CR39","doi-asserted-by":"publisher","unstructured":"Redko I, Courty N, Flamary R, Tuia D (2019) Optimal transport for multi-source domain adaptation under target shift. In: Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, Naha, Okinawa, Japan, pp 849\u2013858. https:\/\/doi.org\/10.48550\/arXiv.1803.04899","DOI":"10.48550\/arXiv.1803.04899"},{"key":"3036_CR40","unstructured":"Pan S J, Kwok J T, Yang Q (2008) Transfer learning via dimensionality reduction. In: Proc 8th AAAI Conf Artif Intell. 8:677\u2013682. https:\/\/cdn.aaai.org\/AAAI\/2008\/AAAI08-108.pdf.\u00a0 Accessed 9 Feb 2024"},{"issue":"5","key":"3036_CR41","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1109\/TPAMI.1982.4767298","volume":"4","author":"R Peck","year":"1982","unstructured":"Peck R, Van Ness J (1982) The use of shrinkage estimators in linear discriminant analysis. IEEE Trans Pattern Anal Mach Intell 4(5):530\u2013537. https:\/\/doi.org\/10.1109\/TPAMI.1982.4767298","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"10","key":"3036_CR42","doi-asserted-by":"publisher","first-page":"1753","DOI":"10.1109\/TNSRE.2016.2627016","volume":"25","author":"F Yger","year":"2017","unstructured":"Yger F, Berar M, Lotte F (2017) Riemannian approaches in brain-computer interfaces: a review. IEEE Trans Neural Syst Rehabil Eng 25(10):1753\u20131762. https:\/\/doi.org\/10.1109\/TNSRE.2016.2627016","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"3036_CR43","doi-asserted-by":"publisher","first-page":"1128","DOI":"10.1109\/TNSRE.2023.3241301","volume":"31","author":"Y Gao","year":"2023","unstructured":"Gao Y, Li M, Peng Y, Fang F, Zhang Y (2023) Double Stage Transfer Learning for Brain-Computer Interfaces. IEEE Trans Neural Syst Rehabil Eng 31:1128\u20131136. https:\/\/doi.org\/10.1109\/TNSRE.2023.3241301","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"3036_CR44","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.neucom.2022.12.048","volume":"523","author":"S Wang","year":"2023","unstructured":"Wang S, Wang B, Zhang Z, Heidair AA, Chen H (2023) Class-aware sample reweighting optimal transport for multi-source domain adaptation. Neurocomputing 523:213\u2013223. https:\/\/doi.org\/10.1016\/j.neucom.2022.12.048","journal-title":"Neurocomputing"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03036-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-024-03036-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03036-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T23:04:09Z","timestamp":1715123049000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-024-03036-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,12]]},"references-count":44,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["3036"],"URL":"https:\/\/doi.org\/10.1007\/s11517-024-03036-9","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,12]]},"assertion":[{"value":"18 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}