{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T09:59:14Z","timestamp":1780912754813,"version":"3.54.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T00:00:00Z","timestamp":1764979200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"vor","delay-in-days":37,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J NeuroEngineering Rehabil"],"DOI":"10.1186\/s12984-025-01833-3","type":"journal-article","created":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T06:06:09Z","timestamp":1765001169000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Characterization of error-related potentials during the command of a lower-limb exoskeleton based on deep learning"],"prefix":"10.1186","volume":"23","author":[{"given":"Paula","family":"Soriano-Segura","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"Ortiz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristina","family":"Polo-Hortig\u00fcela","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eduardo","family":"I\u00e1\u00f1ez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 M.","family":"Azor\u00edn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,6]]},"reference":[{"issue":"54","key":"1833_CR1","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1145\/1941487.1941506","volume":"5","author":"DJ McFarland","year":"2011","unstructured":"McFarland DJ, Wolpaw JR. Brain-computer interfaces for communication and control. Commun ACM. 2011;5(54):60\u20136. https:\/\/doi.org\/10.1145\/1941487.1941506.","journal-title":"Commun ACM"},{"key":"1833_CR2","doi-asserted-by":"crossref","unstructured":"Ortiz M, Nathan K, Azor\u00edn JM, Contreras-Vidal JL. Brain-machine interfaces for neurorobotics. Springer Nature Singapore; 2023.","DOI":"10.1007\/978-981-16-5540-1_52"},{"issue":"7","key":"1833_CR3","doi-asserted-by":"publisher","first-page":"139","DOI":"10.5626\/JCSE.2013.7.2.139","volume":"6","author":"KK Ang","year":"2013","unstructured":"Ang KK, Guan C. Brain-computer interface in stroke rehabilitation. J Comput Sci Eng. 2013;6(7):139\u201346. https:\/\/doi.org\/10.5626\/JCSE.2013.7.2.139.","journal-title":"J Comput Sci Eng"},{"issue":"33","key":"1833_CR4","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1109\/TCST.2024.3477299","volume":"3","author":"H Zhan","year":"2025","unstructured":"Zhan H, Kou J, Guo Q, Wang C, Chen Z, Shi Y, et al. Multilevel control strategy of human-exoskeleton cooperative motion with multimodal wearable training evaluation. IEEE Trans Control Syst Technol. 2025;3(33):434\u201348. https:\/\/doi.org\/10.1109\/TCST.2024.3477299.","journal-title":"IEEE Trans Control Syst Technol"},{"issue":"30","key":"1833_CR5","doi-asserted-by":"publisher","first-page":"2598","DOI":"10.1109\/TMECH.2024.3453037","volume":"8","author":"J Kou","year":"2025","unstructured":"Kou J, Wang Y, Chen Z, Shi Y, Guo Q. Gait planning and multimodal human-exoskeleton cooperative control based on central pattern generator. IEEE\/ASME Trans Mechatron. 2025;8(30):2598\u2013608. https:\/\/doi.org\/10.1109\/TMECH.2024.3453037.","journal-title":"IEEE\/ASME Trans Mechatron"},{"key":"1833_CR6","doi-asserted-by":"crossref","unstructured":"Ortiz M, I\u00e1\u00f1ez E, Gaxiola J, Kilicarslan A, Contreras-Vidal JL, Azor\u00edn JM. Assessment of motor imagery in gamma band using a lower limb exoskeleton. In: 2019 IEEE International conference on systems, man and cybernetics (SMC); 2019;2773\u20132778.","DOI":"10.1109\/SMC.2019.8914483"},{"key":"1833_CR7","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3389\/fnins.2018.00757","volume":"10","author":"M Rodr\u00edguez-Ugarte","year":"2018","unstructured":"Rodr\u00edguez-Ugarte M, I\u00e1\u00f1ez E, Ortiz M, Azor\u00edn JM. Improving real-time lower limb motor imagery detection using tDCS and an exoskeleton. Front Neurosci. 2018;10:12. https:\/\/doi.org\/10.3389\/fnins.2018.00757.","journal-title":"Front Neurosci"},{"issue":"36","key":"1833_CR8","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1177\/15459683221138751","volume":"12","author":"A Colucci","year":"2022","unstructured":"Colucci A, Vermehren M, Cavallo A, Angerh\u00f6fer C, Peekhaus N, Zollo L, et al. Brain-computer interface-controlled exoskeletons in clinical neurorehabilitation: Ready or not? Neurorehabil Neural Repair. 2022;12(36):747\u201356. https:\/\/doi.org\/10.1177\/15459683221138751.","journal-title":"Neurorehabil Neural Repair"},{"issue":"41","key":"1833_CR9","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1016\/j.neuroimage.2008.03.020","volume":"7","author":"M Bakker","year":"2008","unstructured":"Bakker M, Lange FPD, Helmich RC, Scheeringa R, Bloem BR, Toni I. Cerebral correlates of motor imagery of normal and precision gait. Neuroimage. 2008;7(41):998\u20131010. https:\/\/doi.org\/10.1016\/j.neuroimage.2008.03.020.","journal-title":"Neuroimage"},{"issue":"11","key":"1833_CR10","doi-asserted-by":"publisher","first-page":"4106","DOI":"10.3390\/app11094106","volume":"4","author":"L Ferrero","year":"2021","unstructured":"Ferrero L, Quiles V, Ortiz M, I\u00e1\u00f1ez E, Azor\u00edn JM. A BMI based on motor imagery and attention for commanding a lower-limb robotic exoskeleton: a case study. Appl Sci. 2021;4(11):4106. https:\/\/doi.org\/10.3390\/app11094106.","journal-title":"Appl Sci"},{"key":"1833_CR11","doi-asserted-by":"crossref","unstructured":"Ferrero L, I\u00e1\u00f1ez E, Quiles V, Azor\u00edn JM, Ortiz M. Adapting EEG based MI-BMI depending on alertness level for controlling a lower-limb exoskeleton. In: 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE); 2022;399\u2013403.","DOI":"10.1109\/MetroXRAINE54828.2022.9967639"},{"issue":"21","key":"1833_CR12","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/s12984-024-01342-9","volume":"4","author":"L Ferrero","year":"2024","unstructured":"Ferrero L, Soriano-Segura P, Navarro J, Jones O, Ortiz M, I\u00e1\u00f1ez E, et al. Brain\u2013machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study. J Neuroeng Rehabil. 2024;4(21):48. https:\/\/doi.org\/10.1186\/s12984-024-01342-9.","journal-title":"J Neuroeng Rehabil"},{"issue":"55","key":"1833_CR13","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1109\/TBME.2007.908083","volume":"3","author":"PW Ferrez","year":"2008","unstructured":"Ferrez PW, Millan JDR. Error-related EEG potentials generated during simulated brain-computer interaction. IEEE Trans Biomed Eng. 2008;3(55):923\u20139. https:\/\/doi.org\/10.1109\/TBME.2007.908083.","journal-title":"IEEE Trans Biomed Eng"},{"issue":"18","key":"1833_CR14","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1109\/TNSRE.2010.2053387","volume":"8","author":"R Chavarriaga","year":"2010","unstructured":"Chavarriaga R, Millan JDR. Learning from EEG error-related potentials in noninvasive brain-computer interfaces. IEEE Trans Neural Syst Rehab Eng. 2010;8(18):381\u20138. https:\/\/doi.org\/10.1109\/TNSRE.2010.2053387.","journal-title":"IEEE Trans Neural Syst Rehab Eng"},{"key":"1833_CR15","doi-asserted-by":"publisher","first-page":"8","DOI":"10.3389\/fnins.2014.00208","volume":"7","author":"R Chavarriaga","year":"2014","unstructured":"Chavarriaga R, Sobolewski A, Mill\u00e1n JDR. Errare machinale est: the use of error-related potentials in brain-machine interfaces. Frontiers Neurosci. 2014;7:8. https:\/\/doi.org\/10.3389\/fnins.2014.00208.","journal-title":"Frontiers Neurosci"},{"issue":"26","key":"1833_CR16","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/TNSRE.2017.2755018","volume":"1","author":"A Cruz","year":"2018","unstructured":"Cruz A, Pires G, Nunes UJ. Double errp detection for automatic error correction in an ERP-based BCI speller. IEEE Trans Neural Syst Rehabil Eng. 2018;1(26):26\u201336. https:\/\/doi.org\/10.1109\/TNSRE.2017.2755018.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"17","key":"1833_CR17","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abaa9d","volume":"10","author":"M Mousavi","year":"2020","unstructured":"Mousavi M, Krol LR, de Sa VR. Hybrid brain-computer interface with motor imagery and error-related brain activity. J Neural Eng. 2020;10(17):056041. https:\/\/doi.org\/10.1088\/1741-2552\/abaa9d.","journal-title":"J Neural Eng"},{"key":"1833_CR18","doi-asserted-by":"publisher","first-page":"16","DOI":"10.3389\/fnhum.2022.806517","volume":"6","author":"AX Fid\u00eancio","year":"2022","unstructured":"Fid\u00eancio AX, Klaes C, Iossifidis I. Error-related potentials in reinforcement learning-based brain-machine interfaces. Front Hum Neurosci. 2022;6:16. https:\/\/doi.org\/10.3389\/fnhum.2022.806517.","journal-title":"Front Hum Neurosci"},{"issue":"20","key":"1833_CR19","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/acb102","volume":"2","author":"T Tao","year":"2023","unstructured":"Tao T, Jia Y, Xu G, Liang R, Zhang Q, Chen L, et al. Enhancement of motor imagery training efficiency by an online adaptive training paradigm integrated with error related potential. J Neural Eng. 2023;2(20):016029. https:\/\/doi.org\/10.1088\/1741-2552\/acb102.","journal-title":"J Neural Eng"},{"key":"1833_CR20","doi-asserted-by":"crossref","unstructured":"Omedes J, Iturrate I, Chavarriaga R, Montesano L. Asynchronous decoding of error potentials during the monitoring of a reaching task. In: 2015 IEEE International conference on systems, man, and cybernetics; 2015;3116\u20133121.","DOI":"10.1109\/SMC.2015.541"},{"issue":"9","key":"1833_CR21","doi-asserted-by":"publisher","first-page":"17596","DOI":"10.1038\/s41598-019-54109-x","volume":"11","author":"C Lopes-Dias","year":"2019","unstructured":"Lopes-Dias C, Sburlea AI, M\u00fcller-Putz GR. Online asynchronous decoding of error-related potentials during the continuous control of a robot. Sci Rep. 2019;11(9):17596. https:\/\/doi.org\/10.1038\/s41598-019-54109-x.","journal-title":"Sci Rep"},{"key":"1833_CR22","doi-asserted-by":"crossref","unstructured":"Tessadori J, Schiatti L, Barresi G, Mattos LS. Does tactile feedback enhance single-trial detection of error-related eeg potentials? In: 2017 IEEE International conference on systems, man, and cybernetics (SMC); 2017;1417\u20131422.","DOI":"10.1109\/SMC.2017.8122812"},{"key":"1833_CR23","doi-asserted-by":"crossref","unstructured":"Schiatti L, Barresi G, Tessadori J, King LC, Mattos LS. The effect of vibrotactile feedback on ErrP-based adaptive classification of motor imagery. In: 2019 41st Annual international conference of the IEEE engineering in medicine and biology society (EMBC); 2019;6750\u20136753.","DOI":"10.1109\/EMBC.2019.8857192"},{"issue":"34","key":"1833_CR24","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1007\/s10548-021-00840-6","volume":"7","author":"B Ahkami","year":"2021","unstructured":"Ahkami B, Ghassemi F. Adding tactile feedback and changing ISI to improve BCI systems\u2019 robustness: an error-related potential study. Brain Topogr. 2021;7(34):467\u201377. https:\/\/doi.org\/10.1007\/s10548-021-00840-6.","journal-title":"Brain Topogr"},{"key":"1833_CR25","doi-asserted-by":"publisher","first-page":"17","DOI":"10.3389\/fnbot.2023.1297990","volume":"12","author":"SK Kim","year":"2023","unstructured":"Kim SK, Kirchner EA. Detection of tactile-based error-related potentials (ErrPs) in human-robot interaction. Front Neurorobot. 2023;12:17. https:\/\/doi.org\/10.3389\/fnbot.2023.1297990.","journal-title":"Front Neurorobot"},{"key":"1833_CR26","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.cmpb.2024.108332","volume":"10","author":"P Soriano-Segura","year":"2024","unstructured":"Soriano-Segura P, Ortiz M, I\u00e1\u00f1ez E, Azor\u00edn JM. Design of a brain-machine interface for reducing false activations of a lower-limb exoskeleton based on error related potential. Comput Methods Programs Biomed. 2024;10:255. https:\/\/doi.org\/10.1016\/j.cmpb.2024.108332.","journal-title":"Comput Methods Programs Biomed"},{"issue":"26","key":"1833_CR27","doi-asserted-by":"publisher","first-page":"1508","DOI":"10.1109\/TNSRE.2018.2848883","volume":"8","author":"L Yao","year":"2018","unstructured":"Yao L, Mrachacz-Kersting N, Sheng X, Zhu X, Farina D, Jiang N. A multi-class BCI based on somatosensory imagery. IEEE Trans Neural Syst Rehabil Eng. 2018;8(26):1508\u201315. https:\/\/doi.org\/10.1109\/TNSRE.2018.2848883.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"28","key":"1833_CR28","doi-asserted-by":"publisher","first-page":"2773","DOI":"10.1109\/TNSRE.2020.3048106","volume":"12","author":"E Santamaria-Vazquez","year":"2020","unstructured":"Santamaria-Vazquez E, Martinez-Cagigal V, Vaquerizo-Villar F, Hornero R. EEG-inception: a novel deep convolutional neural network for assistive ERP-based brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng. 2020;12(28):2773\u201382. https:\/\/doi.org\/10.1109\/TNSRE.2020.3048106.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"13","key":"1833_CR29","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/13\/2\/026013","volume":"4","author":"A Kilicarslan","year":"2016","unstructured":"Kilicarslan A, Grossman RG, Contreras-Vidal JL. A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurements. J Neural Eng. 2016;4(13):026013. https:\/\/doi.org\/10.1088\/1741-2560\/13\/2\/026013.","journal-title":"J Neural Eng"},{"issue":"8","key":"1833_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2022.e10240","volume":"8","author":"O George","year":"2022","unstructured":"George O, Smith R, Madiraju P, Yahyasoltani N, Ahamed SI. Data augmentation strategies for EEG-based motor imagery decoding. Heliyon. 2022;8(8):e10240. https:\/\/doi.org\/10.1016\/j.heliyon.2022.e10240.","journal-title":"Heliyon"},{"key":"1833_CR31","doi-asserted-by":"crossref","unstructured":"Gao Y, Tao T, Jia Y, et al. Error Related Potential Classification Using a 2-D Convolutional Neural Network. In: Liu H, Yin Z, Liu L, Jiang L, Gu G, Wu X, et al., editors. Intelligent robotics and applications. Cham: Springer International Publishing; 2022;711\u201321.","DOI":"10.1007\/978-3-031-13822-5_64"},{"key":"1833_CR32","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3389\/fninf.2018.00078","volume":"11","author":"P Wierzgala","year":"2018","unstructured":"Wierzgala P, Zapala D, Wojcik GM, Masiak J. Most popular signal processing methods in motor-imagery BCI: a review and meta-analysis. Frontiers Neuroinf. 2018;11:12. https:\/\/doi.org\/10.3389\/fninf.2018.00078.","journal-title":"Frontiers Neuroinf"}],"container-title":["Journal of NeuroEngineering and Rehabilitation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12984-025-01833-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12984-025-01833-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12984-025-01833-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T22:21:13Z","timestamp":1768342873000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12984-025-01833-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,6]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1833"],"URL":"https:\/\/doi.org\/10.1186\/s12984-025-01833-3","relation":{},"ISSN":["1743-0003"],"issn-type":[{"value":"1743-0003","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,6]]},"assertion":[{"value":"21 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 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"}},{"value":"The study was approved by the Responsible Research Office of Miguel Hern\u00e1ndez University of Elche (Spain) (DIS.JAP.09.21). All participants received a detailed explanation of the experiments and they provided written informed consent in accordance with the Helsinki Declaration.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"11"}}