{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T09:12:07Z","timestamp":1784106727292,"version":"3.55.0"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100000065","name":"U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["NS124564, NS131069, NS127849, NS096761"],"award-info":[{"award-number":["NS124564, NS131069, NS127849, NS096761"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000070","name":"U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","award":["EB029365"],"award-info":[{"award-number":["EB029365"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Commun"],"DOI":"10.1038\/s41467-026-75435-5","type":"journal-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T09:01:29Z","timestamp":1784106089000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control"],"prefix":"10.1038","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5852-3275","authenticated-orcid":false,"given":"Hanwen","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yisha","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1854-1879","authenticated-orcid":false,"given":"Maxim","family":"Karrenbach","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9363-7755","authenticated-orcid":false,"given":"Yidan","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2944-8602","authenticated-orcid":false,"given":"Bin","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,15]]},"reference":[{"key":"75435_CR1","doi-asserted-by":"publisher","first-page":"4658","DOI":"10.1016\/j.cell.2025.06.015","volume":"188","author":"EM Kunz","year":"2025","unstructured":"Kunz, E. M. et al. Inner speech in motor cortex and implications for speech neuroprostheses. Cell 188, 4658\u20134673.e17 (2025).","journal-title":"Cell"},{"key":"75435_CR2","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1038\/s41586-019-1119-1","volume":"568","author":"GK Anumanchipalli","year":"2019","unstructured":"Anumanchipalli, G. K., Chartier, J. & Chang, E. F. Speech synthesis from neural decoding of spoken sentences. Nature 568, 493\u2013498 (2019).","journal-title":"Nature"},{"key":"75435_CR3","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1056\/NEJMoa2027540","volume":"385","author":"DA Moses","year":"2021","unstructured":"Moses, D. A. et al. Neuroprosthesis for decoding speech in a paralyzed person with anarthria. N. Engl. J. Med. 385, 217\u2013227 (2021).","journal-title":"N. Engl. J. Med."},{"key":"75435_CR4","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1016\/S0140-6736(12)61816-9","volume":"381","author":"JL Collinger","year":"2013","unstructured":"Collinger, J. L. et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381, 557\u2013564 (2013).","journal-title":"Lancet"},{"key":"75435_CR5","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1038\/nature11076","volume":"485","author":"LR Hochberg","year":"2012","unstructured":"Hochberg, L. R. et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485, 372\u2013375 (2012).","journal-title":"Nature"},{"key":"75435_CR6","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1126\/science.adq5978","volume":"387","author":"G Valle","year":"2025","unstructured":"Valle, G. et al. Tactile edges and motion via patterned microstimulation of the human somatosensory cortex. Science 387, 315\u2013322 (2025).","journal-title":"Science"},{"key":"75435_CR7","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1126\/science.abd0380","volume":"372","author":"SN Flesher","year":"2021","unstructured":"Flesher, S. N. et al. A brain-computer interface that evokes tactile sensations improves robotic arm control. Science 372, 831\u2013836 (2021).","journal-title":"Science"},{"key":"75435_CR8","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/RBME.2024.3449790","volume":"18","author":"BJ Edelman","year":"2025","unstructured":"Edelman, B. J. et al. Non-invasive brain-computer interfaces: state of the Art and trends. IEEE Rev. Biomed. Eng. 18, 26\u201349 (2025).","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"75435_CR9","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1038\/nrneurol.2016.113","volume":"12","author":"U Chaudhary","year":"2016","unstructured":"Chaudhary, U., Birbaumer, N. & Ramos-Murguialday, A. Brain\u2013computer interfaces for communication and rehabilitation. Nat. Rev. Neurol. 12, 513\u2013525 (2016).","journal-title":"Nat. Rev. Neurol."},{"key":"75435_CR10","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1016\/S1388-2457(02)00057-3","volume":"113","author":"JR Wolpaw","year":"2002","unstructured":"Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G. & Vaughan, T. M. Brain\u2013computer interfaces for communication and control. Clin. Neurophysiol. 113, 767\u2013791 (2002).","journal-title":"Clin. Neurophysiol."},{"key":"75435_CR11","doi-asserted-by":"publisher","first-page":"046003","DOI":"10.1088\/1741-2560\/10\/4\/046003","volume":"10","author":"K LaFleur","year":"2013","unstructured":"LaFleur, K. et al. Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain\u2013computer interface. J. Neural Eng. 10, 046003 (2013).","journal-title":"J. Neural Eng."},{"key":"75435_CR12","doi-asserted-by":"publisher","DOI":"10.1038\/srep38565","volume":"6","author":"J Meng","year":"2016","unstructured":"Meng, J. et al. Noninvasive electroencephalogram-based control of a robotic arm for reach and grasp tasks. Sci. Rep. 6, 38565 (2016).","journal-title":"Sci. Rep."},{"key":"75435_CR13","doi-asserted-by":"publisher","first-page":"1510","DOI":"10.1038\/s42256-025-01090-y","volume":"7","author":"JY Lee","year":"2025","unstructured":"Lee, J. Y. et al. Brain\u2013computer interface control with artificial intelligence copilots. Nat. Mach. Intell. 7, 1510\u20131523 (2025).","journal-title":"Nat. Mach. Intell."},{"key":"75435_CR14","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-39814-6","volume":"14","author":"Z Wang","year":"2023","unstructured":"Wang, Z. et al. Conformal in-ear bioelectronics for visual and auditory brain-computer interfaces. Nat. Commun. 14, 4213 (2023).","journal-title":"Nat. Commun."},{"key":"75435_CR15","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-61064-x","volume":"16","author":"Y Ding","year":"2025","unstructured":"Ding, Y., Udompanyawit, C., Zhang, Y. & He, B. EEG-based brain-computer interface enables real-time robotic hand control at the individual finger level. Nat. Commun. 16, 5401 (2025).","journal-title":"Nat. Commun."},{"key":"75435_CR16","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.aaw6844","volume":"4","author":"BJ Edelman","year":"2019","unstructured":"Edelman, B. J. et al. Noninvasive neuroimaging enhances continuous neural tracking for robotic device control. Sci. Robot. 4, eaaw6844 (2019).","journal-title":"Sci. Robot."},{"key":"75435_CR17","doi-asserted-by":"publisher","first-page":"1425","DOI":"10.1109\/TBME.2014.2312397","volume":"61","author":"H Yuan","year":"2014","unstructured":"Yuan, H. & He, B. Brain\u2013computer interfaces using sensorimotor rhythms: current state and future perspectives. IEEE Trans. Biomed. Eng. 61, 1425\u20131435 (2014).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"75435_CR18","doi-asserted-by":"publisher","first-page":"17849","DOI":"10.1073\/pnas.0403504101","volume":"101","author":"JR Wolpaw","year":"2004","unstructured":"Wolpaw, J. R. & McFarland, D. J. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc. Natl. Acad. Sci. 101, 17849\u201317854 (2004).","journal-title":"Proc. Natl. Acad. Sci."},{"key":"75435_CR19","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, B. J., Cline, C. C. & Ye, W. W. Noninvasive brain-computer interfaces based on sensorimotor rhythms. Proc. IEEE 103, 907\u2013925 (2015).","journal-title":"Proc. IEEE"},{"key":"75435_CR20","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.neuroimage.2005.12.003","volume":"31","author":"G Pfurtscheller","year":"2006","unstructured":"Pfurtscheller, G., Brunner, C., Schlogl, A. & Lopes da Silva, F. H. Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage 31, 153\u2013159 (2006).","journal-title":"NeuroImage"},{"key":"75435_CR21","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1109\/TCDS.2021.3053455","volume":"14","author":"H Wang","year":"2021","unstructured":"Wang, H. et al. RCIT: an RSVP-based concealed information test framework using EEG signals. IEEE Trans. Cogn. Dev. Syst. 14, 541\u2013551 (2021).","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"75435_CR22","doi-asserted-by":"publisher","first-page":"6058","DOI":"10.1073\/pnas.1508080112","volume":"112","author":"X Chen","year":"2015","unstructured":"Chen, X. et al. High-speed spelling with a noninvasive brain\u2013computer interface. Proc. Natl. Acad. Sci. 112, 6058\u20136067 (2015).","journal-title":"Proc. Natl. Acad. Sci."},{"key":"75435_CR23","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1002\/ana.24390","volume":"77","author":"F Pichiorri","year":"2015","unstructured":"Pichiorri, F. et al. Brain\u2013computer interface boosts motor imagery practice during stroke recovery. Ann. Neurol. 77, 851\u2013865 (2015).","journal-title":"Ann. Neurol."},{"key":"75435_CR24","doi-asserted-by":"publisher","first-page":"2361","DOI":"10.1113\/JP281314","volume":"599","author":"N Mrachacz-Kersting","year":"2021","unstructured":"Mrachacz-Kersting, N., Ib\u00e1\u00f1ez, J. & Farina, D. Towards a mechanistic approach for the development of non-invasive brain-computer interfaces for motor rehabilitation. J. Physiol. 599, 2361\u20132374 (2021).","journal-title":"J. Physiol."},{"key":"75435_CR25","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1109\/TNSRE.2016.2646763","volume":"25","author":"KK Ang","year":"2016","unstructured":"Ang, K. K. & Guan, C. EEG-based strategies to detect motor imagery for control and rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 25, 392\u2013401 (2016).","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"75435_CR26","doi-asserted-by":"publisher","first-page":"036024","DOI":"10.1088\/1741-2560\/13\/3\/036024","volume":"13","author":"C Jeunet","year":"2016","unstructured":"Jeunet, C., Jahanpour, E. & Lotte, F. Why standard brain-computer interface (BCI) training protocols should be changed: an experimental study. J. Neural Eng. 13, 036024 (2016).","journal-title":"J. Neural Eng."},{"key":"75435_CR27","doi-asserted-by":"publisher","first-page":"10955","DOI":"10.1109\/TNNLS.2022.3172108","volume":"34","author":"C Ju","year":"2022","unstructured":"Ju, C. & Guan, C. Tensor-cspnet: a novel geometric deep learning framework for motor imagery classification. IEEE Trans. Neural Netw. Learn. Syst. 34, 10955\u201310969 (2022).","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"75435_CR28","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TCDS.2020.3007453","volume":"14","author":"D Wu","year":"2020","unstructured":"Wu, D., Xu, Y. & Lu, B.-L. Transfer learning for EEG-based brain\u2013computer interfaces: a review of progress made since 2016. IEEE Trans. Cogn. Dev. Syst. 14, 4\u201319 (2020).","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"75435_CR29","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1093\/pnasnexus\/pgae145","volume":"3","author":"D Forenzo","year":"2024","unstructured":"Forenzo, D., Zhu, H., Shanahan, J., Lim, J. & He, B. Continuous tracking using deep learning-based decoding for noninvasive brain\u2013computer interface. PNAS Nexus 3, 145 (2024).","journal-title":"PNAS Nexus"},{"key":"75435_CR30","doi-asserted-by":"publisher","first-page":"031005","DOI":"10.1088\/1741-2552\/aab2f2","volume":"15","author":"F Lotte","year":"2018","unstructured":"Lotte, F. et al. A review of classification algorithms for EEG-based brain\u2013computer interfaces: a 10-year update. J. Neural Eng. 15, 031005 (2018).","journal-title":"J. Neural Eng."},{"key":"75435_CR31","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1109\/TBME.2022.3201241","volume":"70","author":"Y Zhong","year":"2022","unstructured":"Zhong, Y., Yao, L., Wang, J. & Wang, Y. Tactile sensation-assisted motor imagery training for enhanced BCI performance: a randomized controlled study. IEEE Trans. Biomed. Eng. 70, 694\u2013702 (2022).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"75435_CR32","doi-asserted-by":"publisher","first-page":"1583","DOI":"10.1109\/TNSRE.2021.3102304","volume":"29","author":"NA Grigorev","year":"2021","unstructured":"Grigorev, N. A. et al. A BCI-based vibrotactile neurofeedback training improves motor cortical excitability during motor imagery. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 1583\u20131592 (2021).","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"75435_CR33","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.biopsycho.2015.07.009","volume":"110","author":"M Ninaus","year":"2015","unstructured":"Ninaus, M. et al. Brain volumetry and self-regulation of brain activity relevant for neurofeedback. Biol. Psychol. 110, 126\u2013133 (2015).","journal-title":"Biol. Psychol."},{"key":"75435_CR34","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1093\/cercor\/bhaa234","volume":"31","author":"JR Stieger","year":"2021","unstructured":"Stieger, J. R. et al. Mindfulness improves brain\u2013computer interface performance by increasing control over neural activity in the alpha band. Cereb. Cortex 31, 426\u2013438 (2021).","journal-title":"Cereb. Cortex"},{"key":"75435_CR35","doi-asserted-by":"publisher","first-page":"036005","DOI":"10.1088\/1741-2552\/ac689f","volume":"19","author":"HS Pulferer","year":"2022","unstructured":"Pulferer, H. S., \u00c1sgeirsd\u00f3ttir, B., Mondini, V., Sburlea, A. I. & M\u00fcller-Putz, G. R. Continuous 2D trajectory decoding from attempted movement: across-session performance in able-bodied and feasibility in a spinal cord injured participant. J. Neural Eng. 19, 036005 (2022).","journal-title":"J. Neural Eng."},{"key":"75435_CR36","doi-asserted-by":"publisher","first-page":"036010","DOI":"10.1088\/1741-2552\/ab882e","volume":"17","author":"A Schwarz","year":"2020","unstructured":"Schwarz, A., H\u00f6ller, M. K., Pereira, J., Ofner, P. & M\u00fcller-Putz, G. R. Decoding hand movements from human EEG to control a robotic arm in a simulation environment. J. Neural Eng. 17, 036010 (2020).","journal-title":"J. Neural Eng."},{"key":"75435_CR37","doi-asserted-by":"publisher","first-page":"e2003787","DOI":"10.1371\/journal.pbio.2003787","volume":"16","author":"S Perdikis","year":"2018","unstructured":"Perdikis, S., Tonin, L., Saeedi, S., Schneider, C. & Mill\u00e1n, J. delR. The Cybathlon BCI race: successful longitudinal mutual learning with two tetraplegic users. PLOS Biol. 16, e2003787 (2018).","journal-title":"PLOS Biol."},{"key":"75435_CR38","doi-asserted-by":"publisher","first-page":"036005","DOI":"10.1088\/1741-2552\/aa620b","volume":"14","author":"JS M\u00fcller","year":"2017","unstructured":"M\u00fcller, J. S. et al. A mathematical model for the two-learners problem. J. Neural Eng. 14, 036005 (2017).","journal-title":"J. Neural Eng."},{"key":"75435_CR39","doi-asserted-by":"publisher","unstructured":"Wang, H., et al. A Human\u2013Machine Joint Learning Framework to Boost Endogenous BCI Training. IEEE Trans. Neural Netw. Learn. Syst. https:\/\/doi.org\/10.1109\/TNNLS.2023.3305621 (2023).","DOI":"10.1109\/TNNLS.2023.3305621"},{"key":"75435_CR40","doi-asserted-by":"publisher","unstructured":"Tonin, L., et al Learning to control a BMI-driven wheelchair for people with severe tetraplegia. iScience https:\/\/doi.org\/10.1016\/j.isci.2022.105418 (2022).","DOI":"10.1016\/j.isci.2022.105418"},{"key":"75435_CR41","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1126\/science.aaa8415","volume":"349","author":"MI Jordan","year":"2015","unstructured":"Jordan, M. I. & Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 349, 255\u2013260 (2015).","journal-title":"Science"},{"key":"75435_CR42","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1109\/TBME.2004.827072","volume":"51","author":"G Schalk","year":"2004","unstructured":"Schalk, G., McFarland, D. J., Hinterberger, T., Birbaumer, N. & Wolpaw, J. R. BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51, 1034\u20131043 (2004).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"75435_CR43","doi-asserted-by":"publisher","first-page":"056013","DOI":"10.1088\/1741-2552\/aace8c","volume":"15","author":"VJ Lawhern","year":"2018","unstructured":"Lawhern, V. J. et al. EEGNet: a compact convolutional neural network for EEG-based brain\u2013computer interfaces. J. Neural Eng. 15, 056013 (2018).","journal-title":"J. Neural Eng."},{"key":"75435_CR44","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.neubiorev.2018.08.003","volume":"94","author":"RM Hardwick","year":"2018","unstructured":"Hardwick, R. M., Caspers, S., Eickhoff, S. B. & Swinnen, S. P. Neural correlates of action: comparing meta-analyses of imagery, observation, and execution. Neurosci. Biobehav. Rev. 94, 31\u201344 (2018).","journal-title":"Neurosci. Biobehav. Rev."},{"key":"75435_CR45","doi-asserted-by":"publisher","DOI":"10.34133\/cbsystems.0118","volume":"5","author":"H Wen","year":"2024","unstructured":"Wen, H., Zhong, Y., Yao, L. & Wang, Y. Neural correlates of motor\/tactile imagery and tactile sensation in a BCI paradigm: a high-density EEG source imaging study. Cyborg Bionic Syst. 5, 0118 (2024).","journal-title":"Cyborg Bionic Syst"},{"key":"75435_CR46","doi-asserted-by":"publisher","first-page":"1593","DOI":"10.1126\/science.275.5306.1593","volume":"275","author":"W Schultz","year":"1997","unstructured":"Schultz, W., Dayan, P. & Montague, P. R. A neural substrate of prediction and reward. Science 275, 1593\u20131599 (1997).","journal-title":"Science"},{"key":"75435_CR47","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1152\/jn.00032.2014","volume":"113","author":"AA Nikooyan","year":"2015","unstructured":"Nikooyan, A. A. & Ahmed, A. A. Reward feedback accelerates motor learning. J. Neurophysiol. 113, 633\u2013646 (2015).","journal-title":"J. Neurophysiol."},{"key":"75435_CR48","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1038\/nn.3956","volume":"18","author":"JM Galea","year":"2015","unstructured":"Galea, J. M., Mallia, E., Rothwell, J. & Diedrichsen, J. The dissociable effects of punishment and reward on motor learning. Nat. Neurosci. 18, 597\u2013602 (2015).","journal-title":"Nat. Neurosci."},{"key":"75435_CR49","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1146\/annurev-neuro-060909-153135","volume":"33","author":"R Shadmehr","year":"2010","unstructured":"Shadmehr, R., Smith, M. A. & Krakauer, J. W. Error correction, sensory prediction, and adaptation in motor control. Annu. Rev. Neurosci. 33, 89\u2013108 (2010).","journal-title":"Annu. Rev. Neurosci."},{"key":"75435_CR50","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1002\/j.2040-4603.2019.tb00069.x","volume":"9","author":"JW Krakauer","year":"2019","unstructured":"Krakauer, J. W., Hadjiosif, A. M., Xu, J., Wong, A. L. & Haith, A. M. Motor learning. Compr. Physiol. 9, 613\u2013663 (2019).","journal-title":"Compr. Physiol."},{"key":"75435_CR51","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.jmp.2008.12.005","volume":"53","author":"Y Niv","year":"2021","unstructured":"Niv, Y. Reinforcement learning in the brain. J. Math. Psychol. 53, 139\u2013154 (2021).","journal-title":"J. Math. Psychol."},{"key":"75435_CR52","doi-asserted-by":"publisher","first-page":"3009","DOI":"10.1523\/JNEUROSCI.3205-16.2017","volume":"37","author":"D Luque","year":"2017","unstructured":"Luque, D. et al. Goal-directed and habit-like modulations of stimulus processing during reinforcement learning. J. Neurosci. 37, 3009\u20133017 (2017).","journal-title":"J. Neurosci."},{"key":"75435_CR53","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/MSMC.2019.2958200","volume":"6","author":"S Perdikis","year":"2020","unstructured":"Perdikis, S. & Millan, J. delR. Brain-machine interfaces: a tale of two learners. IEEE Syst. Man Cybern. Mag. 6, 12\u201319 (2020).","journal-title":"IEEE Syst. Man Cybern. Mag."},{"key":"75435_CR54","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1038\/nrn3112","volume":"12","author":"DM Wolpert","year":"2011","unstructured":"Wolpert, D. M., Diedrichsen, J. & Flanagan, J. R. Principles of sensorimotor learning. Nat. Rev. Neurosci. 12, 739\u2013751 (2011).","journal-title":"Nat. Rev. Neurosci."},{"key":"75435_CR55","doi-asserted-by":"publisher","first-page":"1842","DOI":"10.1016\/S1388-2457(99)00141-8","volume":"110","author":"G Pfurtscheller","year":"1999","unstructured":"Pfurtscheller, G. & Lopes da Silva, F. H. Event-related EEG\/MEG synchronization and desynchronization: basic principles. Clin. Neurophysiol. 110, 1842\u20131857 (1999).","journal-title":"Clin. Neurophysiol."},{"key":"75435_CR56","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1016\/j.cogbrainres.2005.08.014","volume":"25","author":"C Neuper","year":"2006","unstructured":"Neuper, C., Scherer, R., Reiner, M. & Pfurtscheller, G. Imagery of motor actions: differential effects of kinesthetic and visual\u2013motor mode of imagery in single-trial EEG. Cogn. Brain Res. 25, 668\u2013677 (2006).","journal-title":"Cogn. Brain Res."},{"key":"75435_CR57","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.tins.2015.12.006","volume":"39","author":"DJ Ostry","year":"2016","unstructured":"Ostry, D. J. & Gribble, P. L. Sensory plasticity in human motor learning. Trends Neurosci 39, 114\u2013123 (2016).","journal-title":"Trends Neurosci"},{"key":"75435_CR58","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1146\/annurev-neuro-112723-042229","volume":"48","author":"M Goulding","year":"2025","unstructured":"Goulding, M., Bollu, T. & B\u00fcschges, A. Sensory feedback and the dynamic control of movement. Annu. Rev. Neurosci. 48, 405\u2013423 (2025).","journal-title":"Annu. Rev. Neurosci."},{"key":"75435_CR59","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-54738-5","volume":"16","author":"B Feulner","year":"2025","unstructured":"Feulner, B., Perich, M. G., Miller, L. E., Clopath, C. & Gallego, J. A. A neural implementation model of feedback-based motor learning. Nat. Commun. 16, 1805 (2025).","journal-title":"Nat. Commun."},{"key":"75435_CR60","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/0166-2236(83)90011-5","volume":"6","author":"RS Johansson","year":"1983","unstructured":"Johansson, R. S. & Vallbo, \u00c5B. Tactile sensory coding in the glabrous skin of the human hand. Trends Neurosci 6, 27\u201332 (1983).","journal-title":"Trends Neurosci"},{"key":"75435_CR61","doi-asserted-by":"publisher","unstructured":"Mountcastle, V B. The Sensory Hand: Neural Mechanisms of Somatic Sensation. https:\/\/doi.org\/10.2307\/j.ctv23dxd9k (Harvard University Press, 2005).","DOI":"10.2307\/j.ctv23dxd9k"},{"key":"75435_CR62","first-page":"3","volume":"3","author":"AB Vallbo","year":"1984","unstructured":"Vallbo, A. B. & Johansson, R. S. Properties of cutaneous mechanoreceptors in the human hand related to touch sensation. Hum Neurobiol 3, 3\u201314 (1984).","journal-title":"Hum Neurobiol"},{"key":"75435_CR63","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1038\/nrn.2016.164","volume":"18","author":"R Sitaram","year":"2017","unstructured":"Sitaram, R. et al. Closed-loop brain training: the science of neurofeedback. Nat. Rev. Neurosci. 18, 86\u2013100 (2017).","journal-title":"Nat. Rev. Neurosci."},{"key":"75435_CR64","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1016\/j.ins.2017.05.043","volume":"414","author":"D Meng","year":"2017","unstructured":"Meng, D., Zhao, Q. & Jiang, L. A theoretical understanding of self-paced learning. Inf. Sci. 414, 319\u2013328 (2017).","journal-title":"Inf. Sci."},{"key":"75435_CR65","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Louradour, J., Collobert, R., & Weston, J. Curriculum learning. Proc. 26th Int. Conf. Mach. Learn. (ICML) 41-48 (2009).","DOI":"10.1145\/1553374.1553380"},{"key":"75435_CR66","doi-asserted-by":"publisher","first-page":"3480","DOI":"10.1109\/TNSRE.2023.3307814","volume":"31","author":"X Mai","year":"2023","unstructured":"Mai, X. et al. A calibration-free hybrid approach combining SSVEP and EOG for continuous control. IEEE Trans. Neural Syst. Rehabil. Eng. 31, 3480\u20133491 (2023).","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"75435_CR67","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-60277-2","volume":"14","author":"M Angrick","year":"2024","unstructured":"Angrick, M. et al. Online speech synthesis using a chronically implanted brain\u2013computer interface in an individual with ALS. Sci. Rep. 14, 9617 (2024).","journal-title":"Sci. Rep."},{"key":"75435_CR68","doi-asserted-by":"publisher","first-page":"016005","DOI":"10.1088\/1741-2560\/6\/1\/016005","volume":"6","author":"AS Royer","year":"2009","unstructured":"Royer, A. S. & He, B. Goal selection versus process control in a brain\u2013computer interface based on sensorimotor rhythms. J. Neural Eng. 6, 016005 (2009).","journal-title":"J. Neural Eng."},{"key":"75435_CR69","doi-asserted-by":"publisher","first-page":"046082","DOI":"10.1088\/1741-2552\/ac0584","volume":"18","author":"JR Stieger","year":"2021","unstructured":"Stieger, J. R., Engel, S. A., Suma, D. & He, B. Benefits of deep learning classification of continuous noninvasive brain\u2013computer interface control. J. Neural Eng. 18, 046082 (2021).","journal-title":"J. Neural Eng."},{"key":"75435_CR70","doi-asserted-by":"publisher","first-page":"2283","DOI":"10.1109\/TNSRE.2022.3198041","volume":"30","author":"H Zhu","year":"2022","unstructured":"Zhu, H., Forenzo, D. & He, B. On the deep learning models for EEG-based brain-computer interface using motor imagery. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 2283\u20132291 (2022).","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"75435_CR71","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1126\/science.1066168","volume":"295","author":"S Makeig","year":"2002","unstructured":"Makeig, S. et al. Dynamic brain sources of visual evoked responses. Science 295, 690\u2013694 (2002).","journal-title":"Science"},{"key":"75435_CR72","doi-asserted-by":"crossref","unstructured":"Selvaraju, R. R., et al. Grad-CAM: visual explanations from deep networks via gradient-based localization. Proc. IEEE Int. Conf. Comput. Vis. (ICCV) 618\u2013626 (2017).","DOI":"10.1109\/ICCV.2017.74"},{"key":"75435_CR73","doi-asserted-by":"publisher","unstructured":"Adebayo, J. et al. Sanity checks for saliency maps. Advances in Neural Information Processing Systems 31 https:\/\/doi.org\/10.48550\/arXiv.1810.03292 (Curran Associates, Inc., 2018).","DOI":"10.48550\/arXiv.1810.03292"},{"key":"75435_CR74","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1038\/s42256-025-01068-w","volume":"7","author":"Z Sun","year":"2025","unstructured":"Sun, Z., Chen, Y.-J., Yang, Y.-H., Li, Y. & Nishida, S. Machine learning modelling for multi-order human visual motion processing. Nat. Mach. Intell. 7, 1037\u20131052 (2025).","journal-title":"Nat. Mach. Intell."},{"key":"75435_CR75","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. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9\u201321 (2004).","journal-title":"J. Neurosci. Methods"},{"key":"75435_CR76","doi-asserted-by":"publisher","unstructured":"Wang, H., Zhang, Y., Karrenbach, M., Ding, Y., & He, B. Code for Sensory-guided joint learning in motor imagery brain-computer interfaces. Zenodo https:\/\/doi.org\/10.5281\/zenodo.20480141 (2026).","DOI":"10.5281\/zenodo.20480141"}],"container-title":["Nature Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41467-026-75435-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41467-026-75435-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41467-026-75435-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T09:01:32Z","timestamp":1784106092000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41467-026-75435-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,15]]},"references-count":76,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["75435"],"URL":"https:\/\/doi.org\/10.1038\/s41467-026-75435-5","relation":{},"ISSN":["2041-1723"],"issn-type":[{"value":"2041-1723","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,15]]},"assertion":[{"value":"20 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"H.W. and B.H. are co-inventors of a provisional patent application (No. 64\/058,661) for the human-machine joint learning and co-adaptive, as well as sample reweighting techniques. The remaining authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"6177"}}