{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T07:24:01Z","timestamp":1777879441057,"version":"3.51.4"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.bspc.2026.110320","type":"journal-article","created":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T12:45:52Z","timestamp":1776516352000},"page":"110320","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["An online active lower-limb rehabilitation training system based on motor imagery fNIRS signals"],"prefix":"10.1016","volume":"121","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8862-5591","authenticated-orcid":false,"given":"Jiayao","family":"Xiang","sequence":"first","affiliation":[]},{"given":"Dandan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8408-3381","authenticated-orcid":false,"given":"Wei","family":"Wei","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2880-3679","authenticated-orcid":false,"given":"Mingyu","family":"Du","sequence":"additional","affiliation":[]},{"given":"Shibo","family":"Cai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4193-5051","authenticated-orcid":false,"given":"Tianyu","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"22","key":"10.1016\/j.bspc.2026.110320_b1","doi-asserted-by":"crossref","first-page":"E183","DOI":"10.1073\/pnas.1101914108","article-title":"Targeted mini-strokes produce changes in interhemispheric sensory signal processing that are indicative of disinhibition within minutes","volume":"108","author":"Mohajerani","year":"2011","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"4","key":"10.1016\/j.bspc.2026.110320_b2","doi-asserted-by":"crossref","DOI":"10.1177\/00368504241301519","article-title":"Evaluation of the effect on stroke mechanism, stroke recurrence and clinical outcome in stroke patients with basilar artery atherosclerosis: A single centre retrospective observational study","volume":"107","author":"Dinc","year":"2024","journal-title":"Sci. Prog."},{"key":"10.1016\/j.bspc.2026.110320_b3","doi-asserted-by":"crossref","DOI":"10.3389\/fneur.2025.1609242","article-title":"Effects of exercise intervention on physical mobility in stroke patients: A scoping review and research progress","volume":"16","author":"Ren","year":"2025","journal-title":"Front. Neurol."},{"key":"10.1016\/j.bspc.2026.110320_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.brainresbull.2025.111354","article-title":"Advances in brain-computer interface controlled functional electrical stimulation for upper limb recovery after stroke","volume":"226","author":"Zhang","year":"2025","journal-title":"Brain Res. Bull."},{"key":"10.1016\/j.bspc.2026.110320_b5","doi-asserted-by":"crossref","DOI":"10.3389\/fneur.2024.1394424","article-title":"Effects of motor imagery-based brain-computer interface-controlled electrical stimulation on lower limb function in hemiplegic patients in the acute phase of stroke: A randomized controlled study","volume":"15","author":"Luo","year":"2024","journal-title":"Front. Neurol."},{"issue":"2","key":"10.1016\/j.bspc.2026.110320_b6","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1007\/s40120-022-00333-z","article-title":"Effects of training with a brain-computer interface-controlled robot on rehabilitation outcome in patients with subacute stroke: A randomized controlled trial","volume":"11","author":"Zhao","year":"2022","journal-title":"Neurol. Ther."},{"key":"10.1016\/j.bspc.2026.110320_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.jneumeth.2024.110132","article-title":"Multimodal brain-controlled system for rehabilitation training: Combining asynchronous online brain\u2013computer interface and exoskeleton","volume":"406","author":"Liu","year":"2024","journal-title":"J. Neurosci. Methods"},{"key":"10.1016\/j.bspc.2026.110320_b8","doi-asserted-by":"crossref","first-page":"117944","DOI":"10.1109\/ACCESS.2024.3443066","article-title":"Enhancing classification accuracy of fNIRS-BCI for gait rehabilitation","volume":"12","author":"Minhas","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2026.110320_b9","doi-asserted-by":"crossref","DOI":"10.1186\/s12984-018-0346-2","article-title":"fNIRS-based neurorobotic interface for gait rehabilitation","volume":"15","author":"Khan","year":"2018","journal-title":"J. Neuroeng. Rehabil."},{"issue":"4","key":"10.1016\/j.bspc.2026.110320_b10","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1109\/TNSRE.2019.2903685","article-title":"BCI monitor enhances electroencephalographic and cerebral hemodynamic activations during motor training","volume":"27","author":"Wang","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.bspc.2026.110320_b11","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.gaitpost.2020.11.014","article-title":"EEG differentiates left and right imagined lower limb movement","volume":"84","author":"Kline","year":"2021","journal-title":"Gait & Posture"},{"key":"10.1016\/j.bspc.2026.110320_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.neulet.2023.137133","article-title":"Recognition of unilateral lower limb movement based on EEG signals with ERP-PCA analysis","volume":"800","author":"Gu","year":"2023","journal-title":"Neurosci. Lett."},{"key":"10.1016\/j.bspc.2026.110320_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.107915","article-title":"An fNIRS-BCI study: Effective channels selection in imagining right and left hand movements via brain functional connectivity","volume":"109","author":"Baghaeifar","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110320_b14","doi-asserted-by":"crossref","DOI":"10.34133\/2021\/9821787","article-title":"Classifying motion intention of step length and synchronous walking speed by functional near-infrared spectroscopy","volume":"2021","author":"Zhu","year":"2021","journal-title":"Cyborg Bionic Syst."},{"key":"10.1016\/j.bspc.2026.110320_b15","article-title":"Decoding of walking imagery and idle state using sparse representation based on fNIRS","volume":"2021","author":"Li","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"10.1016\/j.bspc.2026.110320_b16","article-title":"Investigation of deep convolutional neural network for classification of motor imagery fNIRS signals for BCI applications","volume":"62","author":"Janani","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"issue":"5","key":"10.1016\/j.bspc.2026.110320_b17","doi-asserted-by":"crossref","DOI":"10.3390\/s22051932","article-title":"Analyzing classification performance of fNIRS-BCI for gait rehabilitation using deep neural networks","volume":"22","author":"Hamid","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.bspc.2026.110320_b18","doi-asserted-by":"crossref","DOI":"10.1080\/10255842.2025.2563351","article-title":"Transformed wavelets for motor imagery EEG classification using hybrid CNN-modified vision transformer: An exploratory study of MI EEG","author":"Balendra","year":"2025","journal-title":"Comput. Methods Biomech. Biomed. Eng."},{"issue":"1","key":"10.1016\/j.bspc.2026.110320_b19","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1111\/nyas.15288","article-title":"MI-Mamba: A hybrid motor imagery electroencephalograph classification model with Mamba\u2019s global scanning","volume":"1544","author":"Guo","year":"2025","journal-title":"Ann. New York Acad. Sci."},{"key":"10.1016\/j.bspc.2026.110320_b20","series-title":"Advances in Neural Information Processing Systems","first-page":"115906","article-title":"Provable benefits of complex parameterizations for structured state space models","volume":"vol. 37","author":"Ran-Milo","year":"2024"},{"key":"10.1016\/j.bspc.2026.110320_b21","article-title":"Motor imagery training with neurofeedback from the frontal pole facilitated sensorimotor cortical activity and improved hand dexterity","volume":"Volume 14 - 2020","author":"Ota","year":"2020","journal-title":"Front. Neurosci."},{"issue":"10","key":"10.1016\/j.bspc.2026.110320_b22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0026377","article-title":"Simulation of near-infrared light absorption considering individual head and prefrontal cortex anatomy: Implications for optical neuroimaging","volume":"6","author":"Haeussinger","year":"2011","journal-title":"PLoS One"},{"issue":"5","key":"10.1016\/j.bspc.2026.110320_b23","doi-asserted-by":"crossref","first-page":"N91","DOI":"10.1088\/0031-9155\/51\/5\/N02","article-title":"The modified Beer-Lambert law revisited","volume":"51","author":"Kocsis","year":"2006","journal-title":"Phys. Med. Biol."},{"issue":"10","key":"10.1016\/j.bspc.2026.110320_b24","doi-asserted-by":"crossref","first-page":"2550","DOI":"10.1364\/BOE.3.002550","article-title":"Systematic investigation of changes in oxidized cerebral cytochrome c oxidase concentration during frontal lobe activation in healthy adults","volume":"3","author":"Kolyva","year":"2012","journal-title":"Biomed. Opt. Express"},{"issue":"5","key":"10.1016\/j.bspc.2026.110320_b25","doi-asserted-by":"crossref","DOI":"10.1117\/1.JBO.18.5.056001","article-title":"Continuous correction of differential path length factor in near-infrared spectroscopy","volume":"18","author":"Talukdar","year":"2013","journal-title":"J. Biomed. Opt."},{"issue":"1","key":"10.1016\/j.bspc.2026.110320_b26","doi-asserted-by":"crossref","DOI":"10.1117\/1.NPh.8.1.010802","article-title":"NIRS-KIT: A MATLAB toolbox for both resting-state and task fNIRS data analysis","volume":"8","author":"Hou","year":"2021","journal-title":"Neurophotonics"},{"issue":"5","key":"10.1016\/j.bspc.2026.110320_b27","doi-asserted-by":"crossref","DOI":"10.1088\/1741-2552\/abf187","article-title":"CNN-based classification of fNIRS signals in motor imagery BCI system","volume":"18","author":"Ma","year":"2021","journal-title":"J. Neural Eng."},{"issue":"2","key":"10.1016\/j.bspc.2026.110320_b28","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.neuroimage.2005.08.065","article-title":"A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans","volume":"29","author":"Huppert","year":"2006","journal-title":"NeuroImage"},{"key":"10.1016\/j.bspc.2026.110320_b29","article-title":"fNIRS-based brain-computer interfaces: A review","volume":"9","author":"Naseer","year":"2015","journal-title":"Front. Hum. Neurosci."},{"issue":"1","key":"10.1016\/j.bspc.2026.110320_b30","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/MSP.2008.4408441","article-title":"Optimizing spatial filters for robust EEG single-trial analysis","volume":"25","author":"Blankertz","year":"2008","journal-title":"IEEE Signal Process. Mag."},{"issue":"3","key":"10.1016\/j.bspc.2026.110320_b31","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1080\/2326263X.2017.1297192","article-title":"Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review","volume":"4","author":"Congedo","year":"2017","journal-title":"Brain-Comput. Interfaces"},{"issue":"3","key":"10.1016\/j.bspc.2026.110320_b32","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1038\/nrn2575","article-title":"Complex brain networks: Graph theoretical analysis of structural and functional systems","volume":"10","author":"Bullmore","year":"2009","journal-title":"Nature Rev. Neurosci."},{"key":"10.1016\/j.bspc.2026.110320_b33","series-title":"2020 IEEE International Conference on Systems, Man, and Cybernetics","first-page":"2958","article-title":"EEG-TCNet: An accurate temporal convolutional network for embedded motor-imagery brain\u2013machine interfaces","author":"Ingolfsson","year":"2020"},{"issue":"5","key":"10.1016\/j.bspc.2026.110320_b34","doi-asserted-by":"crossref","DOI":"10.3390\/s25051399","article-title":"Hybrid CNN-GRU models for improved EEG motor imagery classification","volume":"25","author":"Bouchane","year":"2025","journal-title":"Sensors"},{"issue":"17","key":"10.1016\/j.bspc.2026.110320_b35","doi-asserted-by":"crossref","first-page":"5194","DOI":"10.1002\/hbm.25994","article-title":"Spatial\u2013temporal graph convolutional network for alzheimer classification based on brain functional connectivity imaging of electroencephalogram","volume":"43","author":"Shan","year":"2022","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.bspc.2026.110320_b36","article-title":"Investigating priming effects of physical practice on motor imagery-induced event-related desynchronization","volume":"Volume 11 - 2020","author":"Daeglau","year":"2020","journal-title":"Front. Psychol."},{"issue":"3","key":"10.1016\/j.bspc.2026.110320_b37","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1088\/1741-2560\/4\/3\/007","article-title":"Brain\u2013computer interface using a simplified functional near-infrared spectroscopy system","volume":"4","author":"Coyle","year":"2007","journal-title":"J. Neural Eng."},{"issue":"1","key":"10.1016\/j.bspc.2026.110320_b38","article-title":"Motor imagery impairment in postacute stroke patients","volume":"2017","author":"Braun","year":"2017","journal-title":"Neural Plast."},{"key":"10.1016\/j.bspc.2026.110320_b39","article-title":"Variation in brain connectivity during motor imagery and motor execution in stroke patients based on electroencephalography","volume":"Volume 18 - 2024","author":"Guo","year":"2024","journal-title":"Front. Neurosci."},{"issue":"6, SI","key":"10.1016\/j.bspc.2026.110320_b40","doi-asserted-by":"crossref","first-page":"1891","DOI":"10.1007\/s00426-022-01768-7","article-title":"Enhancing motor imagery practice using synchronous action observation","volume":"88","author":"Eaves","year":"2024","journal-title":"Psychol. Res. Psychol. Forschung"},{"issue":"1","key":"10.1016\/j.bspc.2026.110320_b41","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TCDS.2020.3007453","article-title":"Transfer learning for EEG-based brain-computer interfaces: A review of progress made since 2016","volume":"14","author":"Wu","year":"2022","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"issue":"4","key":"10.1016\/j.bspc.2026.110320_b42","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1089\/g4h.2023.0069","article-title":"The technology to enhance patient motivation in virtual reality rehabilitation: A review","volume":"13","author":"Zhang","year":"2024","journal-title":"Games Health J."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426008748?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426008748?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T23:43:49Z","timestamp":1777592629000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426008748"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":42,"alternative-id":["S1746809426008748"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110320","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"An online active lower-limb rehabilitation training system based on motor imagery fNIRS signals","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110320","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110320"}}