{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T05:38:08Z","timestamp":1762580288850,"version":"build-2065373602"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T00:00:00Z","timestamp":1762560000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T00:00:00Z","timestamp":1762560000000},"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":["J Supercomput"],"DOI":"10.1007\/s11227-025-08016-w","type":"journal-article","created":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T05:33:43Z","timestamp":1762580023000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AgileEEG: a lightweight CNN enabling real-time BCI control of a portable rehabilitation exoskeleton"],"prefix":"10.1007","volume":"81","author":[{"given":"Yifan","family":"Wang","sequence":"first","affiliation":[]},{"given":"Jiangfan","family":"Yao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,8]]},"reference":[{"key":"8016_CR1","doi-asserted-by":"publisher","unstructured":"Tseng KC, Wang L, Hsieh C, Wong AMK (2024) Portable robots for upper-limb rehabilitation after stroke: a systematic review and meta-analysis. Annals Med 56(1):2337735. https:\/\/doi.org\/10.1080\/07853890.2024.2337735","DOI":"10.1080\/07853890.2024.2337735"},{"issue":"1","key":"8016_CR2","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1038\/s41598-024-84454-5","volume":"15","author":"N Butsing","year":"2025","unstructured":"Butsing N, Voss JG, Keandoungchun J, Thongniran N, Quinn Griffin MT (2025) Changes of health-related quality of life within 6 months after stroke by clinical and sociodemographic factors. Sci Rep 15(1):416. https:\/\/doi.org\/10.1038\/s41598-024-84454-5","journal-title":"Sci Rep"},{"key":"8016_CR3","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1038\/s41467-024-44723-3","volume":"15","author":"E Donati","year":"2024","unstructured":"Donati E, Valle G (2024) Neuromorphic hardware for somatosensory neuroprostheses. Nat Commun 15:556. https:\/\/doi.org\/10.1038\/s41467-024-44723-3","journal-title":"Nat Commun"},{"issue":"9","key":"8016_CR4","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1161\/CIR.0000000000000757","volume":"141","author":"SS Virani","year":"2020","unstructured":"Virani SS, Alonso A, Benjamin EJ et al (2020) Heart disease and stroke statistics-2020 update: a report from the American heart association. Circulation 141(9):139\u2013596. https:\/\/doi.org\/10.1161\/CIR.0000000000000757","journal-title":"Circulation"},{"key":"8016_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106990","volume":"126","author":"S Kansal","year":"2023","unstructured":"Kansal S, Garg D, Upadhyay A, Mittal S, Talwar GS (2023) Dl-amput-eeg: design and development of the low-cost prosthesis for rehabilitation of upper limb amputees using deep-learning-based techniques. Eng Appl Artif Intell 126:106990. https:\/\/doi.org\/10.1016\/j.engappai.2023.106990","journal-title":"Eng Appl Artif Intell"},{"key":"8016_CR6","doi-asserted-by":"publisher","first-page":"809","DOI":"10.3389\/fnins.2020.00809","volume":"14","author":"S Chen","year":"2020","unstructured":"Chen S, Cao L, Shu X, Wang H, Ding L, Wang S-H, Jia J (2020) Longitudinal electroencephalography analysis in subacute stroke patients during intervention of brain-computer interface with exoskeleton feedback. Front Neurosci 14:809. https:\/\/doi.org\/10.3389\/fnins.2020.00809","journal-title":"Front Neurosci"},{"key":"8016_CR7","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1007\/s11227-025-07319-2","volume":"81","author":"K Singh","year":"2025","unstructured":"Singh K, Singha N, Bhalaik S (2025) CCLNet: multiclass motor imagery EEG decoding through extended common spatial patterns and CNN-LSTM hybrid network. J Supercomput 81:805. https:\/\/doi.org\/10.1007\/s11227-025-07319-2","journal-title":"J Supercomput"},{"key":"8016_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122074","volume":"238","author":"C Zhou","year":"2024","unstructured":"Zhou C, Feng D, Chen S, Ban N, Pan J (2024) Portable vision-based gait assessment for post-stroke rehabilitation using an attention-based lightweight CNN. Expert Syst Appl 238:122074. https:\/\/doi.org\/10.1016\/j.eswa.2023.122074","journal-title":"Expert Syst Appl"},{"key":"8016_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104664","volume":"83","author":"F Wang","year":"2023","unstructured":"Wang F, Wen Y, Bi J, Li H, Sun J (2023) A portable SSVEP-BCI system for rehabilitation exoskeleton in augmented reality environment. Biomed Signal Process Control 83:104664. https:\/\/doi.org\/10.1016\/j.bspc.2023.104664","journal-title":"Biomed Signal Process Control"},{"key":"8016_CR10","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1007\/s11227-024-06627-3","volume":"81","author":"Y Tang","year":"2025","unstructured":"Tang Y, Ma Y, Xiao C et al (2025) Classification of EEG event-related potentials based on channel attention mechanism. J Supercomput 81:126. https:\/\/doi.org\/10.1007\/s11227-024-06627-3","journal-title":"J Supercomput"},{"key":"8016_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.mejo.2024.106134","volume":"145","author":"J Cao","year":"2024","unstructured":"Cao J, Xiong W, Lu J, Chen P, Wang J, Lai J, Huang M (2024) An optimized EEGNET processor for low-power and real-time EEG classification in wearable brain-computer interfaces. Microelectron J 145:106134. https:\/\/doi.org\/10.1016\/j.mejo.2024.106134","journal-title":"Microelectron J"},{"key":"8016_CR12","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1109\/TNSRE.2024.3355488","volume":"32","author":"X Xu","year":"2024","unstructured":"Xu X, Wei F, Jia T, Zhuo L, Zhang H, Li X, Wu X (2024) Embedded EEG feature selection for multi-dimension emotion recognition via local and global label relevance. IEEE Trans Neural Syst Rehabil Eng 32:514\u2013526. https:\/\/doi.org\/10.1109\/TNSRE.2024.3355488","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"8016_CR13","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1186\/s12984-024-01387-w","volume":"21","author":"Z Ma","year":"2024","unstructured":"Ma Z, Wu J, Cao Z, Zhang Y, Zhang L, Chen X (2024) Motor imagery-based brain-computer interface rehabilitation programs enhance upper extremity performance and cortical activation in stroke patients. J Neuroeng Rehabil 21:91. https:\/\/doi.org\/10.1186\/s12984-024-01387-w","journal-title":"J Neuroeng Rehabil"},{"key":"8016_CR14","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1186\/s12883-023-03150-5","volume":"23","author":"X Liu","year":"2023","unstructured":"Liu X, Zhang W, Li W, Zhao H, Li M, Huang Q (2023) Effects of motor imagery based brain-computer interface on upper limb function and attention in stroke patients with hemiplegia: a randomized controlled trial. BMC Neurol 23:136. https:\/\/doi.org\/10.1186\/s12883-023-03150-5","journal-title":"BMC Neurol"},{"key":"8016_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103843","volume":"123","author":"MA Khan","year":"2020","unstructured":"Khan MA, Das R, Iversen HK, Puthusserypady S (2020) Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: from designing to application. Comput Biol Med 123:103843. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103843","journal-title":"Comput Biol Med"},{"key":"8016_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111904","volume":"296","author":"B Lu","year":"2024","unstructured":"Lu B, Huang X, Chen J, Fu R, Wen G (2024) Manifold attention-enhanced multi-domain convolutional network for decoding motor imagery intention. Knowl-Based Syst 296:111904. https:\/\/doi.org\/10.1016\/j.knosys.2024.111904","journal-title":"Knowl-Based Syst"},{"key":"8016_CR17","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1016\/j.future.2019.06.027","volume":"101","author":"SU Amin","year":"2019","unstructured":"Amin SU, Alsulaiman M, Muhammad G, Mekhtiche MA, Shamim Hossain M (2019) Deep learning for EEG motor imagery classification based on multi-layer CNNS feature fusion. Futur Gener Comput Syst 101:542\u2013554. https:\/\/doi.org\/10.1016\/j.future.2019.06.027","journal-title":"Futur Gener Comput Syst"},{"key":"8016_CR18","doi-asserted-by":"publisher","first-page":"1430086","DOI":"10.3389\/fnhum.2024.1430086","volume":"18","author":"J Song","year":"2024","unstructured":"Song J, Zhai Q, Wang C, Liu J (2024) Eeggan-net: enhancing EEG signal classification through data augmentation. Front Hum Neurosci 18:1430086. https:\/\/doi.org\/10.3389\/fnhum.2024.1430086","journal-title":"Front Hum Neurosci"},{"issue":"12","key":"8016_CR19","doi-asserted-by":"publisher","first-page":"2773","DOI":"10.1109\/TNSRE.2020.3048106","volume":"28","author":"E Santamar\u00eda-V\u00e1zquez","year":"2020","unstructured":"Santamar\u00eda-V\u00e1zquez E, Mart\u00ednez-Cagigal V, Vaquerizo-Villar F, Hornero R (2020) EEG-inception: a novel deep convolutional neural network for assistive ERP-based brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng 28(12):2773\u20132782. https:\/\/doi.org\/10.1109\/TNSRE.2020.3048106","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"1","key":"8016_CR20","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1109\/TMRB.2025.3527708","volume":"7","author":"L Campioni","year":"2025","unstructured":"Campioni L, Dimonte G, Sciarrone G, Righi G, Walsh C, Gandolla M, Del Popolo G, Micera S, Proietti T (2025) Preliminary evaluation of a soft wearable robot for shoulder movement assistance. IEEE Trans Med Robot Bion 7(1):315\u2013324. https:\/\/doi.org\/10.1109\/TMRB.2025.3527708","journal-title":"IEEE Trans Med Robot Bion"},{"issue":"3","key":"8016_CR21","doi-asserted-by":"publisher","first-page":"2367","DOI":"10.1109\/LRA.2025.3527307","volume":"10","author":"N Rahman","year":"2025","unstructured":"Rahman N, Diteesawat RS, Hoh S, Morris L, Turton A, Cramp M, Rossiter J (2025) Soft scissor: a cartilage-inspired, pneumatic artificial muscle for wearable devices. IEEE Robot Automat Lett 10(3):2367\u20132374. https:\/\/doi.org\/10.1109\/LRA.2025.3527307","journal-title":"IEEE Robot Automat Lett"},{"key":"8016_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.device.2025.100719","author":"A Shagan Shomron","year":"2025","unstructured":"Shagan Shomron A, Chase-Markopoulou C, Walter JR, Sellhorn-Timm J, Shao Y, Nadler T, Benson A, Wochner I, Rumley EH, Wurster I, Klocke P, Weiss D, Schmitt S, Keplinger C, Haeufle DFB (2025) A robotic and virtual testing platform highlighting the promise of soft wearable actuators for wrist tremor suppression. Device. https:\/\/doi.org\/10.1016\/j.device.2025.100719","journal-title":"Device"},{"key":"8016_CR23","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/s10514-016-9589-6","volume":"41","author":"S Ates","year":"2017","unstructured":"Ates S, Haarman CJW, Stienen AHA (2017) Script passive orthosis: design of interactive hand and wrist exoskeleton for rehabilitation at home after stroke. Auton Robot 41:711\u2013723. https:\/\/doi.org\/10.1007\/s10514-016-9589-6","journal-title":"Auton Robot"},{"issue":"4","key":"8016_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.birob.2024.100176","volume":"4","author":"D Fang","year":"2024","unstructured":"Fang D, Ren F, Wang J, Li P, Cao L, Zhang J (2024) A bionic robotic ankle driven by the multiple pneumatic muscle actuators. Biomimet Intell Robot 4(4):100176. https:\/\/doi.org\/10.1016\/j.birob.2024.100176","journal-title":"Biomimet Intell Robot"},{"key":"8016_CR25","doi-asserted-by":"publisher","unstructured":"Di\u00a0Flumeri G, Aric\u00f2 P, Borghini G, Sciaraffa N, Di\u00a0Florio A, Babiloni F (2019) The dry revolution: evaluation of three different EEG dry electrode types in terms of signal spectral features, mental states classification and usability. Sensors. https:\/\/doi.org\/10.3390\/s19061365","DOI":"10.3390\/s19061365"},{"key":"8016_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.sna.2023.114381","volume":"357","author":"G Na","year":"2023","unstructured":"Na G, Nabae H, Suzumori K (2023) Braided thin mckibben muscles for musculoskeletal robots. Sens Actuators A 357:114381. https:\/\/doi.org\/10.1016\/j.sna.2023.114381","journal-title":"Sens Actuators A"},{"issue":"1","key":"8016_CR27","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","volume":"134","author":"A Delorme","year":"2004","unstructured":"Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134(1):9\u201321. https:\/\/doi.org\/10.1016\/j.jneumeth.2003.10.009","journal-title":"J Neurosci Methods"},{"key":"8016_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102574","volume":"68","author":"P Gaur","year":"2021","unstructured":"Gaur P, McCreadie K, Pachori RB, Wang H, Prasad G (2021) An automatic subject specific channel selection method for enhancing motor imagery classification in EEG-BCI using correlation. Biomed Signal Process Control 68:102574. https:\/\/doi.org\/10.1016\/j.bspc.2021.102574","journal-title":"Biomed Signal Process Control"},{"key":"8016_CR29","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2019.00899","author":"P Waldmann","year":"2019","unstructured":"Waldmann P (2019) On the use of the Pearson correlation coefficient for model evaluation in genome-wide prediction. Front Genet. https:\/\/doi.org\/10.3389\/fgene.2019.00899","journal-title":"Front Genet"},{"key":"8016_CR30","doi-asserted-by":"publisher","unstructured":"Han K, Wang Y, Tian Q, Guo J, Xu C, Xu C (2020) Ghostnet: More features from cheap operations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 1580\u20131589 . https:\/\/doi.org\/10.1109\/CVPR42600.2020.00165","DOI":"10.1109\/CVPR42600.2020.00165"},{"issue":"4","key":"8016_CR31","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aa70d2","volume":"14","author":"F Shiman","year":"2017","unstructured":"Shiman F, L\u00f3pez-Larraz E, Sarasola-Sanz A, Irastorza-Landa N, Sp\u00fcler M, Birbaumer N, Ramos-Murguialday A (2017) Classification of different reaching movements from the same limb using EEG. J Neural Eng 14(4):046018. https:\/\/doi.org\/10.1088\/1741-2552\/aa70d2","journal-title":"J Neural Eng"},{"key":"8016_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105475","volume":"87","author":"X Chen","year":"2024","unstructured":"Chen X, Teng X, Chen H, Pan Y, Geyer P (2024) Toward reliable signals decoding for electroencephalogram: a benchmark study to EEGNEX. Biomed Signal Process Control 87:105475. https:\/\/doi.org\/10.1016\/j.bspc.2023.105475","journal-title":"Biomed Signal Process Control"},{"key":"8016_CR33","doi-asserted-by":"publisher","unstructured":"Schirrmeister R, Gemein L, Eggensperger K, Hutter F, Ball T (2017) Deep learning with convolutional neural networks for decoding and visualization of EEG pathology. In: 2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), pp 1\u20137. https:\/\/doi.org\/10.1109\/SPMB.2017.8257015","DOI":"10.1109\/SPMB.2017.8257015"},{"key":"8016_CR34","doi-asserted-by":"publisher","unstructured":"Milan\u00e9s\u00a0Hermosilla D, Trujillo\u00a0Codorni\u00fa R, L\u00f3pez\u00a0Baracaldo R, Sagar\u00f3\u00a0Zamora R, Delisle\u00a0Rodriguez D, Llosas\u00a0Albuerne Y, \u00c1lvarez JRN (2021) Shallow convolutional network excel for classifying motor imagery eeg in bci applications. IEEE Access 9, 98275\u201398286 https:\/\/doi.org\/10.1109\/ACCESS.2021.3091399","DOI":"10.1109\/ACCESS.2021.3091399"},{"key":"8016_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2025.107200","volume":"185","author":"Y Wang","year":"2025","unstructured":"Wang Y, Wu Z, Yao J, Su J (2025) TDAG: a multi-agent framework based on dynamic task decomposition and agent generation. Neural Netw 185:107200. https:\/\/doi.org\/10.1016\/j.neunet.2025.107200","journal-title":"Neural Netw"},{"issue":"8","key":"8016_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0182578","volume":"12","author":"P Ofner","year":"2017","unstructured":"Ofner P, Schwarz A, Pereira J, M\u00fcller-Putz GR (2017) Upper limb movements can be decoded from the time-domain of low-frequency EEG. PLoS ONE 12(8):1\u201324. https:\/\/doi.org\/10.1371\/journal.pone.0182578","journal-title":"PLoS ONE"},{"key":"8016_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107323","volume":"165","author":"D Borra","year":"2023","unstructured":"Borra D, Mondini V, Magosso E, M\u00fcller-Putz GR (2023) Decoding movement kinematics from EEG using an interpretable convolutional neural network. Comput Biol Med 165:107323. https:\/\/doi.org\/10.1016\/j.compbiomed.2023.107323","journal-title":"Comput Biol Med"},{"key":"8016_CR38","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.ymeth.2021.04.017","volume":"202","author":"J Cui","year":"2022","unstructured":"Cui J, Lan Z, Liu Y, Li R, Li F, Sourina O, M\u00fcller-Wittig W (2022) A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG. Mach Learn Methods Bio-Med Image Signal Process Recent Adv. 202:173\u2013184. https:\/\/doi.org\/10.1016\/j.ymeth.2021.04.017","journal-title":"Mach Learn Methods Bio-Med Image Signal Process Recent Adv."},{"key":"8016_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110034","volume":"135","author":"SK Pati","year":"2023","unstructured":"Pati SK, Banerjee A, Manna S (2023) Gene selection of microarray data using heatmap analysis and graph neural network. Appl Soft Comput 135:110034","journal-title":"Appl Soft Comput"},{"key":"8016_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.126398","volume":"269","author":"M Allaoui","year":"2025","unstructured":"Allaoui M, Belhaouari SB, Hedjam R, Bouanane K, Kherfi ML (2025) t-SNE-PSO: optimizing t-SNE using particle swarm optimization. Expert Syst Appl 269:126398. https:\/\/doi.org\/10.1016\/j.eswa.2025.126398","journal-title":"Expert Syst Appl"},{"issue":"7","key":"8016_CR41","doi-asserted-by":"publisher","first-page":"2212","DOI":"10.1523\/JNEUROSCI.3543-15.2016","volume":"36","author":"J Wagner","year":"2016","unstructured":"Wagner J, Makeig S, Gola M, Neuper C, M\u00fcller-Putz G (2016) Distinct $$\\beta$$ band oscillatory networks subserving motor and cognitive control during gait adaptation. J Neurosci 36(7):2212\u20132226. https:\/\/doi.org\/10.1523\/JNEUROSCI.3543-15.2016","journal-title":"J Neurosci"},{"issue":"1","key":"8016_CR42","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TCDS.2020.3007453","volume":"14","author":"D Wu","year":"2022","unstructured":"Wu D, Xu Y, Lu BL (2022) Transfer learning for EEG-based brain\u2013computer interfaces: a review of progress made since 2016. IEEE Trans Cognit Develop Syst 14(1):4\u201319. https:\/\/doi.org\/10.1109\/TCDS.2020.3007453","journal-title":"IEEE Trans Cognit Develop Syst"},{"key":"8016_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.brainresbull.2025.111354","volume":"226","author":"Y Zhang","year":"2025","unstructured":"Zhang Y, Gao Y, Zhou J, Zhang Z, Feng M, Liu Y (2025) Advances in brain-computer interface controlled functional electrical stimulation for upper limb recovery after stroke. Brain Res Bull 226:111354. https:\/\/doi.org\/10.1016\/j.brainresbull.2025.111354","journal-title":"Brain Res Bull"},{"issue":"3","key":"8016_CR44","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1177\/1545968319827573","volume":"33","author":"A Ramos-Murguialday","year":"2019","unstructured":"Ramos-Murguialday A, Curado M, Broetz D, Yilmaz \u00d6, Brasil F, Liberati G, Garcia-Cossio E, Cho W, Caria A, Cohen L, Birbaumer N (2019) Brain-machine interface in chronic stroke: randomized trial long-term follow-up. Neurorehabil Neural Repair 33(3):188\u2013198. https:\/\/doi.org\/10.1177\/1545968319827573","journal-title":"Neurorehabil Neural Repair"},{"key":"8016_CR45","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2022.971547","volume":"16","author":"M Song","year":"2022","unstructured":"Song M, Jeong H, Kim J, Jang S-H, Kim J (2022) An EEG-based asynchronous MI-BCI system to reduce false positives with a small number of channels for neurorehabilitation: a pilot study. Front Neurorobot 16:971547. https:\/\/doi.org\/10.3389\/fnbot.2022.971547","journal-title":"Front Neurorobot"},{"key":"8016_CR46","doi-asserted-by":"publisher","first-page":"2396","DOI":"10.1038\/srep02396","volume":"3","author":"M Alimardani","year":"2013","unstructured":"Alimardani M, Nishio S, Ishiguro H (2013) Humanlike robot hands controlled by brain activity arouse illusion of ownership in operators. Sci Rep 3:2396. https:\/\/doi.org\/10.1038\/srep02396","journal-title":"Sci Rep"},{"issue":"1","key":"8016_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.rehab.2020.03.015","volume":"64","author":"D Wen","year":"2021","unstructured":"Wen D, Fan Y, Hsu S-H, Xu J, Zhou Y, Tao J, Lan X, Li F (2021) Combining brain\u2013computer interface and virtual reality for rehabilitation in neurological diseases: a narrative review. Ann Phys Rehabil Med 64(1):101404. https:\/\/doi.org\/10.1016\/j.rehab.2020.03.015","journal-title":"Ann Phys Rehabil Med"},{"key":"8016_CR48","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1109\/TNSRE.2022.3230250","volume":"31","author":"Y Song","year":"2023","unstructured":"Song Y, Zheng Q, Liu B, Gao X (2023) EEG conformer: convolutional transformer for EEG decoding and visualization. IEEE Trans Neural Syst Rehabil Eng 31:710\u2013719. https:\/\/doi.org\/10.1109\/TNSRE.2022.3230250","journal-title":"IEEE Trans Neural Syst Rehabil Eng"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08016-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-08016-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08016-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T05:33:44Z","timestamp":1762580024000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-08016-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,8]]},"references-count":48,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["8016"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-08016-w","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,8]]},"assertion":[{"value":"15 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 November 2025","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"1537"}}