{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T20:12:35Z","timestamp":1780517555306,"version":"3.54.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s11517-024-03246-1","type":"journal-article","created":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T13:29:24Z","timestamp":1732886964000},"page":"1059-1079","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["An adaptive session-incremental broad learning system for continuous motor imagery EEG classification"],"prefix":"10.1007","volume":"63","author":[{"given":"Yufei","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0718-8555","authenticated-orcid":false,"given":"Mingai","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linlin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"3246_CR1","doi-asserted-by":"publisher","unstructured":"Majdi H, Azarnoosh M, Ghoshuni M, Sabzevari VR (2024) Direct lingam and visibility graphs for analyzing brain connectivity in BCI. Med Biol Eng Comput 1\u201316. https:\/\/doi.org\/10.1007\/s11517-024-03048-5","DOI":"10.1007\/s11517-024-03048-5"},{"key":"3246_CR2","doi-asserted-by":"publisher","unstructured":"Ma W, Wang C, Sun X, Lin X, Niu L, Wang Y (2023) MBGA-Net: a multi-branch graph adaptive network for individualized motor imagery EEG classification. Compu Methods Prog Biomed 240. https:\/\/doi.org\/10.1016\/j.cmpb.2023.107641","DOI":"10.1016\/j.cmpb.2023.107641"},{"key":"3246_CR3","doi-asserted-by":"publisher","unstructured":"Lu B, Huang X, Chen J, Fu R, Wen G (2024) Manifold attention-enhanced multi-domain convolutional network for decoding motor imagery intention. Knowledge-Based Syst 296. https:\/\/doi.org\/10.1016\/j.knosys.2024.111904","DOI":"10.1016\/j.knosys.2024.111904"},{"key":"3246_CR4","doi-asserted-by":"publisher","unstructured":"Kirkpatrick J, Pascanu R, Rabinowitz N, Veness J, Desjardins G, Rusu AA, ... Hadsell R (2017) Overcoming catastrophic forgetting in neural networks. Proc Nat Acad Sci 114(13), 3521\u20133526. https:\/\/doi.org\/10.1073\/pnas.1611835114","DOI":"10.1073\/pnas.1611835114"},{"issue":"12","key":"3246_CR5","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","volume":"40","author":"Z Li","year":"2017","unstructured":"Li Z, Hoiem D (2017) Learning without forgetting. IEEE Trans Pattern Anal Mach Intell 40(12):2935\u20132947. https:\/\/doi.org\/10.1109\/TPAMI.2017.2773081","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3246_CR6","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/j.neunet.2023.10.039","volume":"169","author":"S Tian","year":"2024","unstructured":"Tian S, Li L, Li W, Ran H, Ning X, Tiwari P (2024) A survey on few-shot class-incremental learning. Neural Netw 169:307\u2013324. https:\/\/doi.org\/10.1016\/j.neunet.2023.10.039","journal-title":"Neural Netw"},{"key":"3246_CR7","doi-asserted-by":"publisher","first-page":"126300","DOI":"10.1016\/j.neucom.2023.126300","volume":"545","author":"S Tian","year":"2023","unstructured":"Tian S, Li W, Ning X, Ran H, Qin H, Tiwari P (2023) Continuous transfer of neural network representational similarity for incremental learning. Neurocomputing 545:126300. https:\/\/doi.org\/10.1016\/j.neucom.2023.126300","journal-title":"Neurocomputing"},{"key":"3246_CR8","doi-asserted-by":"publisher","first-page":"106163","DOI":"10.1016\/j.neunet.2024.106163","volume":"173","author":"G Sun","year":"2024","unstructured":"Sun G, Ji B, Liang L, Chen M (2024) CeCR: Cross-entropy contrastive replay for online class-incremental continual learning. Neural Net 173:106163. https:\/\/doi.org\/10.1016\/j.neunet.2024.106163","journal-title":"Neural Net"},{"key":"3246_CR9","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/j.neunet.2020.05.011","volume":"128","author":"HM Fayek","year":"2020","unstructured":"Fayek HM, Cavedon L, Wu HR (2020) Progressive learning: a deep learning framework for continual learning. Neural Netw 128:345\u2013357. https:\/\/doi.org\/10.1016\/j.neunet.2020.05.011","journal-title":"Neural Netw"},{"key":"3246_CR10","doi-asserted-by":"publisher","unstructured":"Yan S, Xie J, He X (2021) DER: Dynamically expandable representation for class incremental learning. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 3014\u20133023.\u00a0https:\/\/doi.org\/10.1109\/CVPR46437.2021.00303","DOI":"10.1109\/CVPR46437.2021.00303"},{"key":"3246_CR11","doi-asserted-by":"publisher","unstructured":"He J (2024) Gradient reweighting: towards imbalanced class-incremental learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 16668\u201316677.\u00a0https:\/\/doi.org\/10.1109\/CVPR52733.2024.01577","DOI":"10.1109\/CVPR52733.2024.01577"},{"key":"3246_CR12","doi-asserted-by":"publisher","unstructured":"Jodelet Q, Liu X, Phua YJ, Murata T (2023) Class-incremental learning using diffusion model for distillation and replay. In Proceedings of the IEEE\/CVF International Conference on Computer Vision 3425\u20133433.\u00a0https:\/\/doi.org\/10.1109\/ICCVW60793.2023.00367","DOI":"10.1109\/ICCVW60793.2023.00367"},{"key":"3246_CR13","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3419096","author":"Y Lu","year":"2024","unstructured":"Lu Y, Yang L, Chen HR, Cao J, Lin W, Long S (2024) Federated class-incremental learning with dynamic feature extractor fusion. IEEE Trans Mob Comput. https:\/\/doi.org\/10.1109\/TMC.2024.3419096","journal-title":"IEEE Trans Mob Comput"},{"key":"3246_CR14","doi-asserted-by":"publisher","first-page":"110093","DOI":"10.1016\/j.knosys.2022.110093","volume":"260","author":"C Zhang","year":"2023","unstructured":"Zhang C, Tsang EC, Xu W, Lin Y, Yang L (2023) Incremental concept-cognitive learning approach for concept classification oriented to weighted fuzzy concepts. Knowledge-Based Syst 260:110093. https:\/\/doi.org\/10.1016\/j.knosys.2022.110093","journal-title":"Knowledge-Based Syst"},{"key":"3246_CR15","doi-asserted-by":"publisher","first-page":"1208","DOI":"10.1109\/TNSRE.2023.3242280","volume":"31","author":"X Tang","year":"2023","unstructured":"Tang X, Yang C, Sun X, Zou M, Wang H (2023) Motor imagery EEG decoding based on multi-scale hybrid networks and feature enhancement. IEEE Trans Neural Syst Rehabil Eng 31:1208\u20131218. https:\/\/doi.org\/10.1109\/TNSRE.2023.3242280","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"5","key":"3246_CR16","doi-asserted-by":"publisher","first-page":"056013","DOI":"10.1088\/1741-2552\/aace8c","volume":"15","author":"VJ Lawhern","year":"2018","unstructured":"Lawhern VJ, Solon AJ, Waytowich NR, Gordon SM, Hung CP, Lance BJ (2018) EEGNet: a compact convolutional neural network for EEG-based brain\u2013computer interfaces. J Neural Eng 15(5):056013. https:\/\/doi.org\/10.1088\/1741-2552\/aace8c","journal-title":"J Neural Eng"},{"issue":"4","key":"3246_CR17","doi-asserted-by":"publisher","first-page":"598","DOI":"10.3390\/math10040598","volume":"10","author":"M Jim\u00e9nez-Guarneros","year":"2022","unstructured":"Jim\u00e9nez-Guarneros M, Alejo-Eleuterio R (2022) A class-incremental learning method based on preserving the learned feature space for EEG-based emotion recognition. Mathematics 10(4):598. https:\/\/doi.org\/10.3390\/math10040598","journal-title":"Mathematics"},{"key":"3246_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2020.3047502","volume":"70","author":"H Wang","year":"2021","unstructured":"Wang H, Xu L, Bezerianos A, Chen C, Zhang Z (2021) Linking attention-based multiscale CNN with dynamical GCN for driving fatigue detection. IEEE Trans Instrum Meas 70:1\u201311. https:\/\/doi.org\/10.1109\/TIM.2020.3047502","journal-title":"IEEE Trans Instrum Meas"},{"key":"3246_CR19","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3334330","author":"Z Deng","year":"2023","unstructured":"Deng Z, Li C, Song R, Liu X, Qian R, Chen X (2023) Centroid-guided domain incremental learning for EEG-based seizure prediction. IEEE Trans Instrum Meas. https:\/\/doi.org\/10.1109\/TIM.2023.3334330","journal-title":"IEEE Trans Instrum Meas"},{"key":"3246_CR20","doi-asserted-by":"publisher","unstructured":"Li Z, Tan X, Li X, Yin L (2024) Multiclass motor imagery classification with Riemannian geometry and temporal-spectral selection. Med Biol Eng Comput 1\u201313. https:\/\/doi.org\/10.1007\/s11517-024-03103-1. TBWS","DOI":"10.1007\/s11517-024-03103-1"},{"issue":"1","key":"3246_CR21","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s11517-023-02931-x","volume":"62","author":"W Wang","year":"2024","unstructured":"Wang W, Li B, Wang H, Wang X, Qin Y, Shi X, Liu S (2024) EEG-FMCNN: A fusion multi-branch 1D convolutional neural network for EEG-based motor imagery classification. Med Biol Eng Compu 62(1):107\u2013120. https:\/\/doi.org\/10.1007\/s11517-023-02931-x","journal-title":"Med Biol Eng Compu"},{"issue":"1","key":"3246_CR22","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1109\/TNNLS.2017.2716952","volume":"29","author":"CLP Chen","year":"2018","unstructured":"Chen CLP, Liu ZL (2018) Broad learning system: an effective and efficient incremental learning system without the need for deep architecture\/ IEEE Trans. Neural Netw Learn Syst 29(1):10\u201324. https:\/\/doi.org\/10.1109\/TNNLS.2017.2716952","journal-title":"Neural Netw Learn Syst"},{"issue":"12","key":"3246_CR23","doi-asserted-by":"publisher","first-page":"7382","DOI":"10.1109\/TSMC.2020.2969686","volume":"51","author":"S Issa","year":"2021","unstructured":"Issa S, Peng Q, You X (2021) Emotion classification using EEG brain signals and the broad learning system. IEEE Trans Syst Man, Cybernetics: Syst 51(12):7382\u20137391. https:\/\/doi.org\/10.1109\/TSMC.2020.2969686","journal-title":"IEEE Trans Syst Man, Cybernetics: Syst"},{"issue":"9","key":"3246_CR24","doi-asserted-by":"publisher","first-page":"8922","DOI":"10.1109\/TCYB.2021.3061094","volume":"52","author":"X Gong","year":"2021","unstructured":"Gong X, Zhang T, Chen CP, Liu Z (2021) Research review for broad learning system: algorithms, theory, and applications. IEEE Trans Cybernet 52(9):8922\u20138950. https:\/\/doi.org\/10.1109\/TCYB.2021.3061094","journal-title":"IEEE Trans Cybernet"},{"key":"3246_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3377194","author":"J Guo","year":"2024","unstructured":"Guo J, Chen CP, Liu Z, Yang X (2024) Dynamic neural network structure: a review for its theories and applications. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2024.3377194","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3246_CR26","doi-asserted-by":"publisher","unstructured":"Fu R, Liang H, Wang S, Jia C, Sun G, Gao T, ... Wang Y (2024) Transformer-BLS: an efficient learning algorithm based on multi-head attention mechanism and incremental learning algorithms. Exp Syst Appl 238. https:\/\/doi.org\/10.1016\/j.eswa.2023.121734.","DOI":"10.1016\/j.eswa.2023.121734"},{"key":"3246_CR27","doi-asserted-by":"publisher","first-page":"120559","DOI":"10.1016\/j.ins.2024.120559","volume":"669","author":"T Huang","year":"2024","unstructured":"Huang T, Li H, Zhou G, Li S (2024) Stacking multi-view broad learning system with residual structures for classification. Inf Sci 669:120559. https:\/\/doi.org\/10.1016\/j.ins.2024.120559","journal-title":"Inf Sci"},{"issue":"11","key":"3246_CR28","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.3390\/electronics8111273","volume":"8","author":"Q She","year":"2019","unstructured":"She Q, Zhou Y, Gan H, Ma Y, Luo Z (2019) Decoding EEG in motor imagery tasks with graph semi-supervised broad learning. Electronics 8(11):1273. https:\/\/doi.org\/10.3390\/electronics8111273","journal-title":"Electronics"},{"key":"3246_CR29","doi-asserted-by":"publisher","first-page":"10597","DOI":"10.1007\/s00521-021-05793-2","volume":"33","author":"Y Zhou","year":"2021","unstructured":"Zhou Y, She Q, Ma Y, Kong W, Zhang Y (2021) Transfer of semi-supervised broad learning system in electroencephalography signal classification. Neural Comput Appl 33:10597\u201310613. https:\/\/doi.org\/10.1007\/s00521-021-05793-2","journal-title":"Neural Comput Appl"},{"key":"3246_CR30","doi-asserted-by":"publisher","unstructured":"Yang Y, Li M, Liu H, Li Z (2024) A session-incremental broad learning system for motor imagery EEG classification. Biomed Signal Process Cont 97. https:\/\/doi.org\/10.1016\/j.bspc.2024.106717","DOI":"10.1016\/j.bspc.2024.106717"},{"key":"3246_CR31","doi-asserted-by":"publisher","unstructured":"Rebuffi SA, Kolesnikov A, Sperl G, Lampert CH (2017) icarl: Incremental classifier and representation learning. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition 2001\u20132010.\u00a0https:\/\/doi.org\/10.1109\/CVPR.2017.587","DOI":"10.1109\/CVPR.2017.587"},{"key":"3246_CR32","doi-asserted-by":"publisher","unstructured":"Chen R, Chen G, Liao X, Xiong W (2024) Class-incremental learning via prototype similarity replay and similarity-adjusted regularization. Appl Intell 1\u201316.\u00a0https:\/\/doi.org\/10.1007\/s10489-024-05695-5","DOI":"10.1007\/s10489-024-05695-5"},{"key":"3246_CR33","doi-asserted-by":"publisher","first-page":"127204","DOI":"10.1016\/j.neucom.2023.127204","volume":"572","author":"X Li","year":"2024","unstructured":"Li X, Tang B, Li H (2024) AdaER: An adaptive experience replay approach for continual lifelong learning. Neurocomputing 572:127204. https:\/\/doi.org\/10.1016\/j.neucom.2023.127204","journal-title":"Neurocomputing"},{"issue":"12","key":"3246_CR34","doi-asserted-by":"publisher","first-page":"13040","DOI":"10.1007\/s10489-024-05695-5","volume":"38","author":"H Kang","year":"2024","unstructured":"Kang H, Choi DW (2024) Recall-oriented continual learning with generative adversarial meta-model. In Proceedings of the AAAI Conf Artificial Intell 38(12):13040\u201313048. https:\/\/doi.org\/10.1007\/s10489-024-05695-5","journal-title":"In Proceedings of the AAAI Conf Artificial Intell"},{"key":"3246_CR35","doi-asserted-by":"publisher","unstructured":"He C, Wang R, Shan S, Chen X (2024) Introspective GAN: learning to grow a GAN for incremental generation and classification. Pattern Recognit 151. https:\/\/doi.org\/10.1016\/j.patcog.2024.110383","DOI":"10.1016\/j.patcog.2024.110383"},{"key":"3246_CR36","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.comcom.2023.12.030","volume":"215","author":"H Yang","year":"2024","unstructured":"Yang H, He W, Shan Z, Fang X, Chen X (2024) Class incremental learning via dynamic regeneration with task-adaptive distillation. Comput Commun 215:130\u2013139. https:\/\/doi.org\/10.1016\/j.comcom.2023.12.030","journal-title":"Comput Commun"},{"key":"3246_CR37","doi-asserted-by":"publisher","unstructured":"Mallya A, Davis D, Lazebnik S (2018) Piggyback: adapting a single network to multiple tasks by learning to mask weights. In Proceedings of the European conference on computer vision (ECCV) 67\u201382.\u00a0https:\/\/doi.org\/10.48550\/arXiv.1801.06519","DOI":"10.48550\/arXiv.1801.06519"},{"key":"3246_CR38","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.neunet.2022.10.030","volume":"159","author":"F Du","year":"2023","unstructured":"Du F, Yang Y, Zhao Z, Zeng Z (2023) Efficient perturbation inference and expandable network for continual learning. Neural Netw 159:97\u2013106","journal-title":"Neural Netw"},{"key":"3246_CR39","doi-asserted-by":"publisher","unstructured":"Qazi MA, Almakky I, Hashmi AUR, Sanjeev S, Yaqub M (2024) Dynammo: dynamic model merging for efficient class incremental learning for medical images. In Annual Conference on Medical Image Understanding and Analysis 245\u2013257.\u00a0https:\/\/doi.org\/10.1007\/978-3-031-66955-2_17","DOI":"10.1007\/978-3-031-66955-2_17"},{"key":"3246_CR40","doi-asserted-by":"publisher","unstructured":"Dhar P, Singh RV, Peng KC, Wu Z, Chellappa R (2019) Learning without memorizing. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 5138\u20135146.\u00a0https:\/\/doi.org\/10.48550\/arXiv.1811.080","DOI":"10.48550\/arXiv.1811.080"},{"key":"3246_CR41","doi-asserted-by":"publisher","first-page":"127587","DOI":"10.1016\/j.neucom.2024.127587","volume":"584","author":"F Akmel","year":"2024","unstructured":"Akmel F, Meng F, Wu Q, Chen S, Zhang R, Assefa M (2024) Class similarity weighted knowledge distillation for few shot incremental learning. Neurocomputing 584:127587. https:\/\/doi.org\/10.1016\/j.neucom.2024.127587","journal-title":"Neurocomputing"},{"key":"3246_CR42","doi-asserted-by":"publisher","unstructured":"Serra J, Suris D, Miron M, Karatzoglou A (2018) Overcoming catastrophic forgetting with hard attention to the task. In International conference on machine learning 4548\u20134557.\u00a0https:\/\/doi.org\/10.48550\/arXiv.1801.01423","DOI":"10.48550\/arXiv.1801.01423"},{"key":"3246_CR43","doi-asserted-by":"publisher","first-page":"117386","DOI":"10.1016\/j.eswa.2022.117386","volume":"202","author":"R Fu","year":"2022","unstructured":"Fu R, Wang Y, Jia C (2022) A new data aug-mentation method for EEG features based on the hy-brid model of broad-deep networks. Exp Syst Appl 202:117386. https:\/\/doi.org\/10.1016\/j.eswa.2022.117386","journal-title":"Exp Syst Appl"},{"issue":"11","key":"3246_CR44","doi-asserted-by":"publisher","first-page":"6152","DOI":"10.1007\/s00034-022-02071-x","volume":"41","author":"E Dasan","year":"2022","unstructured":"Dasan E, Gnanaraj R (2022) Joint ECG\u2013EMG\u2013EEG signal compression and reconstruction with incre-mental multimodal autoencoder approach. Circuits Sys Signal Process 41(11):6152\u20136181. https:\/\/doi.org\/10.1007\/s00034-022-02071-x","journal-title":"Circuits Sys Signal Process"},{"key":"3246_CR45","doi-asserted-by":"publisher","first-page":"104433","DOI":"10.1016\/j.bspc.2022.104433","volume":"81","author":"JS Garc\u00eda-Salinas","year":"2023","unstructured":"Garc\u00eda-Salinas JS, Torres-Garc\u00eda AA, Reyes-Gar\u0107ia CA, Villase\u00f1or-Pineda L (2023) Intra-subject class-incremental deep learning approach for EEG-based imagined speech recognition. Biomed Signal Process Control 81:104433. https:\/\/doi.org\/10.1016\/j.bspc.2022.104433","journal-title":"Biomed Signal Process Control"},{"key":"3246_CR46","doi-asserted-by":"publisher","unstructured":"Deng Z, Mao T, Shao C, Li C, Chen X (2023) Domain incremental learning for EEG-based seizure prediction. CAAI International Conference on Artificial Intelligence 476\u2013487.\u00a0https:\/\/doi.org\/10.1007\/978-981-99-9119-8_43","DOI":"10.1007\/978-981-99-9119-8_43"},{"issue":"4","key":"3246_CR47","doi-asserted-by":"publisher","first-page":"1872","DOI":"10.1109\/JBHI.2023.3247861","volume":"28","author":"L Sun","year":"2023","unstructured":"Sun L, Zhang M, Wang B, Tiwari P (2023) Few-shot class-incremental learning for medical time series classification. IEEE J Biomed Health Inform 28(4):1872\u20131882","journal-title":"IEEE J Biomed Health Inform"},{"key":"3246_CR48","doi-asserted-by":"publisher","first-page":"106338","DOI":"10.1016\/j.neunet.2024.106338","volume":"176","author":"T Duan","year":"2024","unstructured":"Duan T, Wang Z, Li F, Doretto G, Adjeroh DA, Yin Y, Tao C (2024) Online continual decoding of streaming EEG signal with a balanced and informative memory buffer. Neural Netw 176:106338","journal-title":"Neural Netw"},{"key":"3246_CR49","doi-asserted-by":"publisher","unstructured":"Graves A, Graves A (2012) Long short-term memory. Supervised sequence labelling with recurrent neural networks. 37\u201345. https:\/\/doi.org\/10.1007\/978-3-642-24797-2","DOI":"10.1007\/978-3-642-24797-2"},{"key":"3246_CR50","doi-asserted-by":"publisher","first-page":"122286","DOI":"10.1016\/j.eswa.2023.122286","volume":"238","author":"B Lu","year":"2024","unstructured":"Lu B, Wang F, Wang S, Chen J, Wen G, Fu R (2024) Improvement of motor imagery electroencephalogram decoding by iterative weighted sparse-group lasso. Expert Syst Appl 238:122286","journal-title":"Expert Syst Appl"},{"key":"3246_CR51","doi-asserted-by":"publisher","first-page":"106092","DOI":"10.1016\/j.bspc.2024.106092","volume":"92","author":"J Cao","year":"2024","unstructured":"Cao J, Li G, Shen J, Dai C (2024) IFBCLNet: Spatio-temporal frequency feature extraction-based MI-EEG classification convolutional network. Bio-med Signal Process Control 92:106092. https:\/\/doi.org\/10.1016\/j.bspc.2024.106092","journal-title":"Bio-med Signal Process Control"},{"issue":"25","key":"3246_CR52","doi-asserted-by":"publisher","first-page":"39349","DOI":"10.1007\/s11042-023-15065-x","volume":"82","author":"L Zeng","year":"2023","unstructured":"Zeng L, Tang H, Wang W, Xie M, Ai Z, Chen L, Wu Y (2023) MAMC-Net: an effective deep learning framework for whole-slide image tumor segmentation. Multimed Tools Appl 82(25):39349\u201339369. https:\/\/doi.org\/10.1007\/s11042-023-15065-x","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"3246_CR53","doi-asserted-by":"publisher","first-page":"2423","DOI":"10.1109\/TCE.2023.3330423","volume":"70","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Li P, Cheng L, Li M, Li H (2024) Attention-based multiscale spatial-temporal convolutional network for motor imagery EEG decoding. IEEE Trans Consum Electron 70(1):2423\u20132434. https:\/\/doi.org\/10.1109\/TCE.2023.3330423","journal-title":"IEEE Trans Consum Electron"},{"key":"3246_CR54","doi-asserted-by":"publisher","unstructured":"Liu W, Guo C, Gao C (2024) A cross-session motor imagery classification method based on Riemannian geometry and deep domain adaptation. Exp Syst Appl 237. https:\/\/doi.org\/10.1016\/j.eswa.2023.121612.","DOI":"10.1016\/j.eswa.2023.121612"},{"key":"3246_CR55","first-page":"1","volume":"16","author":"C Brunner","year":"2008","unstructured":"Brunner C, Leeb R, M\u00fcller-Putz G et al (2008) BCI Competition 2008\u2013graz data set A, institute for knowledge Discovery (Laboratory of Brain-Computer Interfaces). Graz Univ Technol 16:1\u20136","journal-title":"Graz Univ Technol"},{"key":"3246_CR56","first-page":"1","volume":"16","author":"R Leeb","year":"2008","unstructured":"Leeb R et al (2008) BCI Competition 2008\u2013Graz data set B. Graz University of Technology, Austria 16:1\u20136","journal-title":"Graz University of Technology, Austria"},{"issue":"2","key":"3246_CR57","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1109\/TNNLS.2021.3100583","volume":"34","author":"E Jeon","year":"2021","unstructured":"Jeon E, Ko W, Yoon JS, Suk HI (2021) Mutual information-driven subject-invariant and class-relevant deep representation learning in BCI. IEEE Trans Neural Netw Learn Syst 34(2):739\u2013749. https:\/\/doi.org\/10.1109\/TNNLS.2021.3100583","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3246_CR58","doi-asserted-by":"publisher","first-page":"2769","DOI":"10.1007\/s11517-024-03096-x","volume":"62","author":"A Zaman","year":"2024","unstructured":"Zaman A, Kumar S, Shatabda S et al (2024) SleepBoost: a multi-level tree-based ensemble model for automatic sleep stage classification. Med Biol Eng Comput 62:2769\u20132783. https:\/\/doi.org\/10.1007\/s11517-024-03096-x","journal-title":"Med Biol Eng Comput"},{"issue":"9","key":"3246_CR59","doi-asserted-by":"publisher","first-page":"10731","DOI":"10.1109\/TPAMI.2023.3262853","volume":"45","author":"D Li","year":"2023","unstructured":"Li D, Zeng Z (2023) CRNet: a fast continual learning framework with random theory. IEEE Trans Pattern Anal Mach Intell 45(9):10731\u201310744. https:\/\/doi.org\/10.1109\/TPAMI.2023.3262853","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3246_CR60","doi-asserted-by":"publisher","unstructured":"Zheng Y, Qin X, Xi Z, et al (2019) Mixed-norm based broad learning system for EEG classification, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 7092\u20137095. https:\/\/doi.org\/10.1109\/EMBC.2019.8856666.","DOI":"10.1109\/EMBC.2019.8856666"},{"issue":"1","key":"3246_CR61","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1109\/TETCI.2023.3301385","volume":"8","author":"X Li","year":"2024","unstructured":"Li X, Tang X, Qiu S, Deng X, Wang H, Tian Y (2024) Subdomain adversarial network for motor imagery EEG classification using graph data. IEEE Trans Emerg Topics Comput Intell 8(1):327\u2013336. https:\/\/doi.org\/10.1109\/TETCI.2023.3301385","journal-title":"IEEE Trans Emerg Topics Comput Intell"},{"key":"3246_CR62","doi-asserted-by":"publisher","unstructured":"Wang C, Wu Y, Wang C, Zhu Y, Wang C, Niu Y, ... Yu Y (2022) MI-EEG classification using Shannon complex wavelet and convolutional neural networks. Appl Soft Comput 130. https:\/\/doi.org\/10.1016\/j.asoc.2022.109685","DOI":"10.1016\/j.asoc.2022.109685"},{"key":"3246_CR63","doi-asserted-by":"publisher","unstructured":"Li H, Zhang D, Xie J (2023) MI-DABAN: a dual-attention-based adversarial network for motor imagery classification. Comput Biol Med 152. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106420","DOI":"10.1016\/j.compbiomed.2022.106420"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03246-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-024-03246-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03246-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T04:07:39Z","timestamp":1743221259000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-024-03246-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,29]]},"references-count":63,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["3246"],"URL":"https:\/\/doi.org\/10.1007\/s11517-024-03246-1","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,29]]},"assertion":[{"value":"17 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 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"}}]}}