Neurotechnology in Stroke Neurorehabilitation: Towards Precision and Personalized Rehabilitation Treatment

Abstract

Given the large global population of stroke survivors and limited rehabilitation resources, efficient treatments are urgently needed to help patients regain independence and reintegrate into society. In this review, we discuss how artificial intelligence and neurotechnology can be used to accelerate neurorehabilitation after stroke. First, we introduce neurorehabilitation mechanisms that provide the basis for neurotechnology development. Next, we describe how neurophysiological and neuroimaging biomarkers can be used for multimodal assessment and prognostic prediction. We then provide examples of brain-computer interface (BCI)-driven rehabilitation robots and BCI-triggered transcranial and peripheral neuromodulation for closed-loop rehabilitation training.

Keywords

Stroke rehabilitation / Neuromodulation / Rehabilitation robotics / Prognostic prediction / Brain-computer interface

References

[1] Feigin VL, Brainin M, Norrving B, Martins SO, Pandian J, Lindsay P, et al. World Stroke Organization: Global Stroke Fact Sheet 2025. Int. J. Stroke 2025, 20, 132-144. DOI: 10.1177/17474930241308142

[2] Bonkhoff AK, Grefkes C. Precision medicine in stroke: Towards personalized outcome predictions using artificial intelligence. Brain 2022, 145, 457-475. DOI: 10.1093/brain/awab439

[3] Stinear CM, Lang CE, Zeiler S, Byblow WD. Advances and challenges in stroke rehabilitation. Lancet Neurol. 2020, 19, 348-360. DOI: 10.1016/S1474-4422(19)30415-6

[4] Fox MD. Mapping Symptoms to Brain Networks with the Human Connectome. N. Engl. J. Med. 2018, 379, 2237-2245. DOI: 10.1056/NEJMra1706158

[5] Stinear CM.Prediction of motor recovery after stroke: Advances in biomarkers. Lancet Neurol. 2017, 16, 826-836. DOI: 10.1016/S1474-4422(17)30283-1

[6] Jia T, Li C, Mo L, Qian C, Li W, Xu Q, et al. Tailoring brain-machine interface rehabilitation training based on neural reorganization: Towards personalized treatment for stroke patients. Cereb. Cortex 2023, 33, 3043-3052. DOI: 10.1093/cercor/bhac259

[7] Soekadar SR, Birbaumer N, Slutzky MW, Cohen LG. Brain-machine interfaces in neurorehabilitation of stroke. Neurobiol. Dis. 2015, 83, 172-179. DOI: 10.1016/j.nbd.2014.11.025

[8] Chaudhary U, Birbaumer N, Ramos-Murguialday A. Brain-computer interfaces for communication and rehabilitation. Nat. Rev. Neurol. 2016, 12, 513-525. DOI: 10.1038/nrneurol.2016.113

[9] Cheng N, Phua KS, Lai HS, Tam PK, Tang KY, Cheng KK,et al. Brain-Computer Interface-Based Soft Robotic Glove Rehabilitation for Stroke. IEEE Trans. Biomed. Eng. 2020, 67, 3339-3351. DOI: 10.1109/TBME.2020.2984003

[10] Jia T, Pan F, Yang X, Ji L, Farina D, Li C. Artificial Empathy in Therapy and Healthcare: Advancements in Interpersonal Interaction Technologies. Cyborg Bionic Syst. 2025, 6, 473. DOI: 10.34133/cbsystems.0473

[11] Pekna M, Pekny M, Nilsson M . Modulation of Neural Plasticity as a Basis for Stroke Rehabilitation. Stroke 2012, 43, 2819-2828. DOI: 10.1161/STROKEAHA.112.654228

[12] Warraich Z, Kleim JA. Neural Plasticity: The Biological Substrate For Neurorehabilitation. PM R 2010, 2, S208-S219. DOI: 10.1016/j.pmrj.2010.10.016

[13] Wolpaw JR. Harnessing neuroplasticity for clinical applications. Brain 2012, 135, e215. DOI: 10.1093/brain/aws017

[14] Holtmaat A, Svoboda K . Experience-dependent structural synaptic plasticity in the mammalian brain. Nat. Rev. Neurosci. 2009, 10, 647-658. DOI: 10.1038/nrn2699

[15] Hordacre B, Austin D, Brown KE, Graetz L, Pareés I, De Trane S, et al. Evidence for a Window of Enhanced Plasticity in the Human Motor Cortex Following Ischemic Stroke. Neurorehabil. Neural Repair 2021, 35, 307-320. DOI: 10.1177/1545968321992330

[16] Richards LJ, Huang C, Bauer AQ, Lee JM. Long-range axon branching: Contributions to brain network plasticity and repair. Nat. Rev. Neurosci. 2026, 27, 243-259. DOI: 10.1038/s41583-025-01008-y

[17] Murphy TH, Corbett D. Plasticity during stroke recovery: From synapse to behaviour. Nat. Rev. Neurosci. 2009, 10, 861-872. DOI: 10.1038/nrn2735

[18] Di Pino G, Pellegrino G, Assenza G, Capone F, Ferreri F, Formica D,et al.Modulation of brain plasticity in stroke: A novel model for neurorehabilitation. Nat. Rev. Neurol. 2014, 10, 597-608. DOI: 10.1038/nrneurol.2014.162

[19] Yan W, Lin Y, Chen YF, Wang Y, Wang J, Zhang M. Enhancing Neuroplasticity for Post-Stroke Motor Recovery: Mechanisms, Models, and Neurotechnology. IEEE Trans. Neural Syst. Rehabil. Eng. 2025, 33, 1156-1168. DOI: 10.1109/TNSRE.2025.3551753

[20] Braaß H, Wolf S, Feldheim J, Chu Y, Tinnermann A, Finsterbusch J, et al. Altered Functional Connectivity Between Cortical Premotor Areas and the Spinal Cord in Chronic Stroke. Stroke 2025, 56, 1159-1168. DOI: 10.1161/STROKEAHA.124.048384

[21] Emmenegger TM, Pfyffer D, Curt A, Schading-Sassenhausen S, Hupp M, Ashburner J, et al. Longitudinal motor system changes from acute to chronic spinal cord injury. Eur. J. Neurol. 2024, 31, e16196. DOI: 10.1111/ene.16196

[22] Freund P, Weiskopf N, Ashburner J, Wolf K, Sutter R, Altmann DR,et al.MRI investigation of the sensorimotor cortex and the corticospinal tract after acute spinal cord injury: A prospective longitudinal study. Lancet Neurol. 2013, 12, 873-881. DOI: 10.1016/S1474-4422(13)70146-7

[23] Khatibi A, Vahdat S, Lungu O, Finsterbusch J, Büchel C, Cohen-Adad J, et al. Brain-spinal cord interaction in long-term motor sequence learning in human: An fMRI study. NeuroImage 2022, 253, 119111. DOI: 10.1016/j.neuroimage.2022.119111

[24] Liu H, Peng X, Dahmani L, Wang H, Zhang M, Shan Y,et al.Patterns of motor recovery and structural neuroplasticity after basal ganglia infarcts. Neurology 2020, 95, e1174-e1187. DOI: 10.1212/WNL.0000000000010149

[25] McDonnell MN, Stinear CM . TMS measures of motor cortex function after stroke: A meta-analysis. Brain Stimul. 2017, 10, 721-734. DOI: 10.1016/j.brs.2017.03.008

[26] Volz LJ, Sarfeld AS, Diekhoff S, Rehme AK, Pool EM, Eickhoff SB, et al. Motor cortex excitability and connectivity in chronic stroke: A multimodal model of functional reorganization. Brain Struct. Funct. 2015, 220, 1093-1107. DOI: 10.1007/s00429-013-0702-8

[27] Yu P, Dong R, Wang X, Tang Y, Liu Y, Wang C, et al. Neuroimaging of motor recovery after ischemic stroke-Functional reorganization of motor network. NeuroImage Clin. 2024, 43, 103636. DOI: 10.1016/j.nicl.2024.103636

[28] Cui F, Zhao L, Lu M, Liu R, Lv Q, Lin D, et al. Functional and structural brain reorganization in patients with ischemic stroke: A multimodality MRI fusion study. Cereb. Cortex 2023, 33, 10453-10462. DOI: 10.1093/cercor/bhad295

[29] Paul T, Wiemer VM, Hensel L, Cieslak M, Tscherpel C, Grefkes C, et al. Interhemispheric Structural Connectivity Underlies Motor Recovery after Stroke. Ann. Neurol. 2023, 94, 785-797. DOI: 10.1002/ana.26737

[30] Zhang K, Ding L, Wang X, Zhuang J, Tong S, Jia J, et al. Evidence of mirror therapy for recruitment of ipsilateral motor pathways in stroke recovery: A resting fMRI study. Neurotherapeutics 2024, 21, e00320. DOI: 10.1016/j.neurot.2024.e00320

[31] Henderson LA, Gustin SM, Macey PM, Wrigley PJ, Siddall PJ . Functional Reorganization of the Brain in Humans Following Spinal Cord Injury: Evidence for Underlying Changes in Cortical Anatomy. J. Neurosci. 2011, 31, 2630-2637. DOI: 10.1523/JNEUROSCI.2717-10.2011

[32] Byblow WD, Stinear CM, Barber PA, Petoe MA, Ackerley SJ. Proportional recovery after stroke depends on corticomotor integrity. Ann. Neurol. 2015, 78, 848-859. DOI: 10.1002/ana.24472

[33] Stinear CM, Barber PA, Smale PR, Coxon JP, Fleming MK, Byblow WD. Functional potential in chronic stroke patients depends on corticospinal tract integrity. Brain 2006, 130, 170-180. DOI: 10.1093/brain/awl333

[34] Escudero JV, Sancho J, Bautista D, Escudero M, López-Trigo J. Prognostic Value of Motor Evoked Potential Obtained by Transcranial Magnetic Brain Stimulation in Motor Function Recovery in Patients with Acute Ischemic Stroke. Stroke 1998, 29, 1854-1859. DOI: 10.1161/01.STR.29.9.1854

[35] Rehme AK, Fink GR, von Cramon DY, Grefkes C . The Role of the Contralesional Motor Cortex for Motor Recovery in the Early Days after Stroke Assessed with Longitudinal fMRI. Cereb. Cortex 2011, 21, 756-768. DOI: 10.1093/cercor/bhq140

[36] Cassidy JM, Wodeyar A, Wu J, Kaur K, Masuda AK, Srinivasan R, et al. Low-Frequency Oscillations Are a Biomarker of Injury and Recovery After Stroke. Stroke 2020, 51, 1442-1450. DOI: 10.1161/STROKEAHA.120.028932

[37] Finnigan S, van Putten MJAM. EEG in ischaemic stroke: Quantitative EEG can uniquely inform (sub-)acute prognoses and clinical management. Clin. Neurophysiol. 2013, 124, 10-19. DOI: 10.1016/j.clinph.2012.07.003

[38] Nicolo P, Rizk S, Magnin C, Pietro MD, Schnider A, Guggisberg AG. Coherent neural oscillations predict future motor and language improvement after stroke. Brain 2015, 138, 3048-3060. DOI: 10.1093/brain/awv200

[39] Paolo M. TMS-evoked N100 responses as a prognostic factor in acute stroke. Funct. Neurol. 2015, 30, 125-130. DOI: 10.11138/FNeur/2015.30.2.125

[40] Tscherpel C, Dern S, Hensel L, Ziemann U, Fink GR, Grefkes C. Brain responsivity provides an individual readout for motor recovery after stroke. Brain 2020, 143, 1873-1888. DOI: 10.1093/brain/awaa127

[41] Casula EP, Pellicciari MC, Bonnì S, Spanò B, Ponzo V, Salsano I, et al. Evidence for interhemispheric imbalance in stroke patients as revealed by combining transcranial magnetic stimulation and electroencephalography. Hum. Brain Mapp. 2021, 42, 1343-1358. DOI: 10.1002/hbm.25297

[42] Sun J, Zhang Q, Ma D, Jia T, Jia S, Zhai X, et al. Beta-Band Corticomuscular Coherence: A Novel Biomarker of Functional Corticospinal Tract Integrity and Motor Recovery After Stroke. IEEE Trans. Neural Syst. Rehabil. Eng. 2025, 33, 3856-3865. DOI: 10.1109/TNSRE.2025.3613417

[43] Buch ER, Rizk S, Nicolo P, Cohen LG, Schnider A, Guggisberg AG . Predicting motor improvement after stroke with clinical assessment and diffusion tensor imaging. Neurology 2016, 86, 1924-1925. DOI: 10.1212/WNL.0000000000002675

[44] Kumar P, Kathuria P, Nair P, Prasad K. Prediction of Upper Limb Motor Recovery after Subacute Ischemic Stroke Using Diffusion Tensor Imaging: A Systematic Review and Meta-Analysis. J. Stroke 2016, 18, 50-59. DOI: 10.5853/jos.2015.01186

[45] Puig J, Blasco G, Daunis-I-Estadella J, Thomalla G, Castellanos M, Figueras J, et al. Decreased Corticospinal Tract Fractional Anisotropy Predicts Long-term Motor Outcome After Stroke. Stroke 2013, 44, 2016-2018. DOI: 10.1161/STROKEAHA.111.000382

[46] Feng W, Wang J, Chhatbar PY, Doughty C, Landsittel D, Lioutas V, et al. Corticospinal tract lesion load: An imaging biomarker for stroke motor outcomes. Ann. Neurol. 2015, 78, 860-870. DOI: 10.1002/ana.24510

[47] Buch ER, Modir Shanechi A, Fourkas AD, Weber C, Birbaumer N, Cohen LG . Parietofrontal integrity determines neural modulation associated with grasping imagery after stroke. Brain 2012, 135, 596-614. DOI: 10.1093/brain/awr331

[48] Schulz R, Park E, Lee J, Chang WH, Lee A, Kim YH,et al.Interactions Between the Corticospinal Tract and Premotor-Motor Pathways for Residual Motor Output after Stroke. Stroke 2017, 48, 2805-2811. DOI: 10.1161/STROKEAHA.117.016834

[49] Guo L, Kondapavulur S, Lemke SM, Won SJ, Ganguly K. Coordinated increase of reliable cortical and striatal ensemble activations during recovery after stroke. Cell Rep. 2021, 36, 109370. DOI: 10.1016/j.celrep.2021.109370

[50] Sauerbrei BA, Guo JZ, Cohen JD, Mischiati M, Guo W, Kabra M, et al. Cortical pattern generation during dexterous movement is input-driven. Nature 2020, 577, 386-391. DOI: 10.1038/s41586-019-1869-9

[51] Schulz R, Frey BM, Koch P, Zimerman M, Bönstrup M, Feldheim J,et al.Cortico-Cerebellar Structural Connectivity Is Related to Residual Motor Output in Chronic Stroke. Cereb. Cortex 2017, 27, 635-645. DOI: 10.1093/cercor/bhv251

[52] Calautti C, Baron JC. Functional Neuroimaging Studies of Motor Recovery After Stroke in Adults. Stroke 2003, 34, 1553-1566. DOI: 10.1161/01.STR.0000071761.36075.A6

[53] Xu Q, Shao Z, Ma D, Zhai X, Wang Y, Dou W, et al. Predicting rehabilitation outcomes of unilateral stroke after brain-computer interface training based on magnetic resonance imaging data. Medicine 2026, 105, e46280. DOI: 10.1097/MD.0000000000046280

[54] Puig J, Blasco G, Alberich-Bayarri A, Schlaug G, Deco G, Biarnes C, et al. Resting-State Functional Connectivity Magnetic Resonance Imaging and Outcome After Acute Stroke. Stroke 2018, 49, 2353-2360. DOI: 10.1161/STROKEAHA.118.021319

[55] Grefkes C, Fink GR. Connectivity-based approaches in stroke and recovery of function. Lancet Neurol. 2014, 13, 206-216. DOI: 10.1016/S1474-4422(13)70264-3

[56] Vucic S, Stanley Chen KH, Kiernan MC, Hallett M, Benninger DH, Di Lazzaro V, et al. Clinical diagnostic utility of transcranial magnetic stimulation in neurological disorders. Updated report of an IFCN committee. Clin. Neurophysiol. 2023, 150, 131-175. DOI: 10.1016/j.clinph.2023.03.010

[57] Ackerley S, Smith MC, Jordan H, Stinear CM . Biomarkers of Motor Outcomes After Stroke. Phys. Med. Rehabil. Clin. North. Am. 2024, 35, 259-276. DOI: 10.1016/j.pmr.2023.06.003

[58] Smith MC, Scrivener BJ, Stinear CM. Do lower limb motor-evoked potentials predict walking outcomes post-stroke? J. Neurol. Neurosurg. Psychiatry 2024, 95, 348-355. DOI: 10.1136/jnnp-2023-332018

[59] Smith MC, Byblow WD, Barber PA, Stinear CM. Proportional Recovery From Lower Limb Motor Impairment after Stroke. Stroke 2017, 48, 1400-1403. DOI: 10.1161/STROKEAHA.116.016478

[60] Delcamp C, Srinivasan R, Cramer SC. EEG Provides Insights Into Motor Control and Neuroplasticity During Stroke Recovery. Stroke 2024, 55, 2579-2583. DOI: 10.1161/STROKEAHA.124.048458

[61] Vatinno AA, Simpson A, Ramakrishnan V, Bonilha HS, Bonilha L, Seo NJ.The Prognostic Utility of Electroencephalography in Stroke Recovery: A Systematic Review and Meta-Analysis. Neurorehabil. Neural Repair 2022, 36, 255-268. DOI: 10.1177/15459683221078294

[62] Cervera MA, Soekadar SR, Ushiba J, Millán JDR, Liu M, Birbaumer N, et al. Brain-computer interfaces for post-stroke motor rehabilitation: A meta-analysis. Ann. Clin. Transl. Neurol. 2018, 5, 651-663. DOI: 10.1002/acn3.544

[63] Feydy A, Carlier R, Roby-Brami A, Bussel B, Cazalis F, Pierot L, et al. Longitudinal Study of Motor Recovery After Stroke. Stroke 2002, 33, 1610-1617. DOI: 10.1161/01.STR.0000017100.68294.52

[64] Platz T, Kim IH, Engel U, Kieselbach A, Mauritz KH. Brain activation pattern as assessed with multi-modal EEG analysis predict motor recovery among stroke patients with mild arm paresis who receive the Arm Ability Training. Restor. Neurol. Neurosci. 2002, 20, 21-35. DOI: 10.3233/RNN-2002-00212

[65] Keser Z, Buchl SC, Seven NA, Markota M, Clark HM, Jones DT, et al. Electroencephalogram (EEG) With or Without Transcranial Magnetic Stimulation (TMS) as Biomarkers for Post-stroke Recovery: A Narrative Review. Front. Neurol. 2022, 13, 827866. DOI: 10.3389/fneur.2022.827866

[66] Liu J, Sheng Y, Liu H. Corticomuscular Coherence and Its Applications: A Review. Front. Hum. Neurosci. 2019, 13, 100. DOI: 10.3389/fnhum.2019.00100

[67] Mima T, Toma K, Koshy B, Hallett M . Coherence Between Cortical and Muscular Activities After Subcortical Stroke. Stroke 2001, 32, 2597-2601. DOI: 10.1161/hs1101.098764

[68] Xu R, Zhang H, Shi X, Liang J, Wan C, Ming D. Lower-Limb Motor Assessment With Corticomuscular Coherence of Multiple Muscles During Ankle Dorsiflexion After Stroke. IEEE Trans. Neural Syst. Rehabil. Eng. 2023, 31, 160-168. DOI: 10.1109/TNSRE.2022.3217571

[69] Belardinelli P, Laer L, Ortiz E, Braun C, Gharabaghi A. Plasticity of premotor cortico-muscular coherence in severely impaired stroke patients with hand paralysis. NeuroImage Clin. 2017, 14, 726-733. DOI: 10.1016/j.nicl.2017.03.005

[70] Khademi F, Naros G, Nicksirat A, Kraus D, Gharabaghi A . Rewiring Cortico-Muscular Control in the Healthy and Poststroke Human Brain with Proprioceptive β-Band Neurofeedback. J. Neurosci. 2022, 42, 6861-6877. DOI: 10.1523/JNEUROSCI.1530-20.2022

[71] Sun J, Xie R, Yu J, Ji L, Jia T, Pan Y, et al. Dynamic modulation of corticomuscular coherence during ankle dorsiflexion after stroke: Towards hybrid BCI for lower-limb rehabilitation. J. Neural Eng. 2026, 23, 016019. DOI: 10.1088/1741-2552/ae3c41

[72] Puig J, Blasco G, Schlaug G, Stinear CM, Daunis-i-Estadella P, Biarnes C, et al. Diffusion tensor imaging as a prognostic biomarker for motor recovery and rehabilitation after stroke. Neuroradiology 2017, 59, 343-351. DOI: 10.1007/s00234-017-1816-0

[73] Boyd LA, Hayward KS, Ward NS, Stinear CM, Rosso C, Fisher RJ,et al.Biomarkers of Stroke Recovery: Consensus-Based Core Recommendations from the Stroke Recovery and Rehabilitation Roundtable. Neurorehabil. Neural Repair 2017, 31, 864-876. DOI: 10.1177/1545968317732680

[74] Siegel JS, Ramsey LE, Snyder AZ, Metcalf NV, Chacko RV, Weinberger K, et al. Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke. Proc. Natl. Acad. Sci. USA 2016, 113, E4367. DOI: 10.1073/pnas.1521083113

[75] Bowren M, Bruss J, Manzel K, Edwards D, Liu C, Corbetta M, et al. Post-stroke outcomes predicted from multivariate lesion-behaviour and lesion network mapping. Brain 2022, 145, 1338-1353. DOI: 10.1093/brain/awac010

[76] Wiemer VM, Paul T, Hensel L, Cieslak M, Tscherpel C, Grefkes C, et al. Structural reserve-dependent cortical rerouting of motor control after stroke. Brain 2026, 149, 2505-2515. DOI: 10.1093/brain/awaf434

[77] Campagnini S, Arienti C, Patrini M, Liuzzi P, Mannini A, Carrozza MC . Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: A systematic review. J. NeuroEng. Rehabil. 2022, 19, 54. DOI: 10.1186/s12984-022-01032-4

[78] Stinear CM, Barber PA, Petoe M, Anwar S, Byblow WD. The PREP algorithm predicts potential for upper limb recovery after stroke. Brain 2012, 135, 2527-2535. DOI: 10.1093/brain/aws146

[79] Koch PJ, Park CH, Girard G, Beanato E, Egger P, Evangelista GG, et al. The structural connectome and motor recovery after stroke: Predicting natural recovery. Brain 2021, 144, 2107-2119. DOI: 10.1093/brain/awab082

[80] Nishi H, Oishi N, Ishii A, Ono I, Ogura T, Sunohara T,et al.Deep Learning-Derived High-Level Neuroimaging Features Predict Clinical Outcomes for Large Vessel Occlusion. Stroke 2020, 51, 1484-1492. DOI: 10.1161/STROKEAHA.119.028101

[81] Karakis R, Gurkahraman K, Mitsis GD, Boudrias MH . Deep learning prediction of motor performance in stroke individuals using neuroimaging data. J. Biomed. Inform. 2023, 141, 104357. DOI: 10.1016/j.jbi.2023.104357

[82] Lin PJ, Li W, Zhai X, Li Z, Sun J, Xu Q, et al. Explainable Deep-Learning Prediction for Brain-Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion. IEEE Trans. Neural Syst. Rehabil. Eng. 2024, 32, 1546-1555. DOI: 10.1109/TNSRE.2024.3384498

[83] Lin PJ, Jia T, Li C, Li T, Qian C, Li Z, et al. CNN-Based Prognosis of BCI Rehabilitation Using EEG From First Session BCI Training. IEEE Trans. Neural Syst. Rehabil. Eng. 2021, 29, 1936-1943. DOI: 10.1109/TNSRE.2021.3112167

[84] Lin PJ, Zhai X, Li W, Li T, Cheng D, Li C, et al. A Transferable Deep Learning Prognosis Model for Predicting Stroke Patients' Recovery in Different Rehabilitation Trainings. IEEE J. Biomed. Health Inform. 2022, 26, 6003-6011. DOI: 10.1109/JBHI.2022.3205436

[85] Stinear CM, Byblow WD, Ackerley SJ, Smith M, Borges VM, Barber PA. PREP2: A biomarker-based algorithm for predicting upper limb function after stroke. Ann. Clin. Transl. Neurol. 2017, 4, 811-820. DOI: 10.1002/acn3.488

[86] Angelov PP, Soares EA, Jiang R, Arnold NI, Atkinson PM . Explainable artificial intelligence: An analytical review. WIREs Data Min. Knowl. 2021, 11, e1424. DOI: 10.1002/widm.1424

[87] Gandolfi M, Boscolo Galazzo I, Gasparin Pavan R, Cruciani F, Vale N, Picelli A, et al. eXplainable AI Allows Predicting Upper Limb Rehabilitation Outcomes in Sub-Acute Stroke Patients. IEEE J. Biomed. Health Inform. 2023, 27, 263-273. DOI: 10.1109/JBHI.2022.3220179

[88] Sun J, Lin PJ, Zhai X, Wang W, Jia T, Li Z, et al. Multimodal Behavioral Data Predict Stroke Patient’s Response to BCI Treatment Through Explainable AI. IEEE Trans. Neural Syst. Rehabil. Eng. 2026, 34, 1-9. DOI: 10.1109/TNSRE.2025.3625222

[89] Rodgers H, Bosomworth H, Krebs HI, van Wijck F, Howel D, Wilson N,et al.Robot assisted training for the upper limb after stroke (RATULS): A multicentre randomised controlled trial. Lancet 2019, 394, 51-62. DOI: 10.1016/S0140-6736(19)31055-4

[90] Li D, Li R, Song Y, Qin W, Sun G, Liu Y, et al. Effects of brain-computer interface based training on post-stroke upper-limb rehabilitation: A meta-analysis. J. NeuroEng. Rehabil. 2025, 22, 44. DOI: 10.1186/s12984-025-01588-x

[91] Hogan N, Krebs HI, Charnnarong J, Srikrishna P, Sharon A . MIT-MANUS: A workstation for manual therapy and training. I. In Proceedings of the International Workshop on Robot and Human Communication, Tokyo, Japan, 1-3 September 1992; pp. 161-165. DOI: 10.1109/ROMAN.1992.253895

[92] Ang KK, Chua KSG, Phua KS, Wang C, Chin ZY, Kuah CWK, et al. A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke. Clin. EEG Neurosci. 2015, 46, 310-320. DOI: 10.1177/1550059414522229

[93] Frisoli A, Salsedo F, Bergamasco M, Rossi B, Carboncini MC. A Force-Feedback Exoskeleton for Upper-Limb Rehabilitation in Virtual Reality. Appl. Bionics Biomech. 2009, 6, 115-126. DOI: 10.1080/11762320902959250

[94] Bhagat NA, Venkatakrishnan A, Abibullaev B, Artz EJ, Yozbatiran N, Blank AA, et al. Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors. Front. Neurosci. 2016, 10, 122. DOI: 10.3389/fnins.2016.00122

[95] Wu B. A brain-computer-interface driven forearm exoskeleton with adaptive neuroregulation-based feedback for stroke rehabilitation. Alex. Eng. J. 2025, 131, 199-208. DOI: 10.1016/j.aej.2025.09.069

[96] Zongxing L, Jie Z, Ligang Y, Jinshui C, Hongbin L . The Human-Machine Interaction Methods and Strategies for Upper and Lower Extremity Rehabilitation Robots: A Review. IEEE Sens. J. 2024, 24, 13773-13787. DOI: 10.1109/JSEN.2024.3374344

[97] Zhai X, Hao Z, Wang X, Li C, Cao Y, Peng B, et al. Effect of Brain-Computer Interface-Controlled Ankle Robot Training on Post-Stroke Motor Rehabilitation and Resting QEEG Neuroplasticity: A Randomized Controlled Trial. Neurorehabil. Neural Repair 2026, 40, 278-290. DOI: 10.1177/15459683251412286

[98] Guo N, Wang X, Duanmu D, Huang X, Li X, Fan Y,et al.SSVEP-Based Brain Computer Interface Controlled Soft Robotic Glove for Post-Stroke Hand Function Rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 2022, 30, 1737-1744. DOI: 10.1109/TNSRE.2022.3185262

[99] Zhang R, Feng S, Hu N, Low S, Li M, Chen X,et al.Hybrid Brain-Computer Interface Controlled Soft Robotic Glove for Stroke Rehabilitation. IEEE J. Biomed. Health Inform. 2024, 28, 4194-4203. DOI: 10.1109/JBHI.2024.3392412

[100] Ji X, Lu X, Xu Y, Zhang W, Yang H, Yin C, et al. Effects and neural mechanisms of a brain-computer interface-controlled soft robotic glove on upper limb function in patients with subacute stroke: A randomized controlled fNIRS study. J. NeuroEng. Rehabil. 2025, 22, 171. DOI: 10.1186/s12984-025-01704-x

[101] Qu H, Zeng F, Tang Y, Shi B, Wang Z, Chen X,et al.The clinical effects of brain-computer interface with robot on upper-limb function for post-stroke rehabilitation: A meta-analysis and systematic review. Disabil. Rehabil. Assist. Technol. 2024, 19, 30-41. DOI: 10.1080/17483107.2022.2060354

[102] Barsi GI, Popovic DB, Tarkka IM, Sinkjær T, Grey MJ. Cortical excitability changes following grasping exercise augmented with electrical stimulation. Exp. Brain Res. 2008, 191, 57-66. DOI: 10.1007/s00221-008-1495-5

[103] Biasiucci A, Leeb R, Iturrate I, Perdikis S, Al-Khodairy A, Corbet T, et al. Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nat. Commun. 2018, 9, 2421. DOI: 10.1038/s41467-018-04673-z

[104] Ibáñez J, Monge-Pereira E, Molina-Rueda F, Serrano JI, del Castillo MD, Cuesta-Gómez A, et al. Low Latency Estimation of Motor Intentions to Assist Reaching Movements along Multiple Sessions in Chronic Stroke Patients: A Feasibility Study. Front. Neurosci. 2017, 11, 126. DOI: 10.3389/fnins.2017.00126

[105] Sebastián-Romagosa M, Cho W, Ortner R, Murovec N, Von Oertzen T, Kamada K, et al. Brain Computer Interface Treatment for Motor Rehabilitation of Upper Extremity of Stroke Patients-A Feasibility Study. Front. Neurosci. 2020, 14, 591435. DOI: 10.3389/fnins.2020.591435

[106] Page SJ, Fulk GD, Boyne P. Clinically Important Differences for the Upper-Extremity Fugl-Meyer Scale in People with Minimal to Moderate Impairment Due to Chronic Stroke. Phys. Ther. 2012, 92, 791-798. DOI: 10.2522/ptj.20110009

[107] Ren C, Li X, Gao Q, Pan M, Wang J, Yang F, et al. The effect of brain-computer interface controlled functional electrical stimulation training on rehabilitation of upper limb after stroke: A systematic review and meta-analysis. Front. Hum. Neurosci. 2024, 18, 1438095. DOI: 10.3389/fnhum.2024.1438095

[108] Wu J, Quinlan EB, Dodakian L, McKenzie A, Kathuria N, Zhou RJ, et al. Connectivity measures are robust biomarkers of cortical function and plasticity after stroke. Brain 2015, 138, 2359-2369. DOI: 10.1093/brain/awv156

[109] Markram H. A history of spike-timing-dependent plasticity. Front. Syn. Neurosci. 2011, 3, 4. DOI: 10.3389/fnsyn.2011.00004

[110] Mrachacz-Kersting N, Jiang N, Stevenson AJT, Niazi IK, Kostic V, Pavlovic A, et al. Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface. J. Neurophysiol. 2016, 115, 1410-1421. DOI: 10.1152/jn.00918.2015

[111] Jia T, Mo L, Li C, Liu A, Li Z, Ji L. 5 Hz rTMS improves motor-imagery based BCI classification performance. In Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Virtual, 1-5 November 2021; pp. 6116-6120. DOI: 10.1109/EMBC46164.2021.9630102

[112] Jia T, Mo L, McGeady C, Sun J, Liu A, Ji L,et al.Cortical Activation Patterns Determine Effectiveness of rTMS-Induced Motor Imagery Decoding Enhancement in Stroke Patients. IEEE Trans. Biomed. Eng. 2025, 72, 1200-1208. DOI: 10.1109/TBME.2024.3492977

[113] Hong X,Lu ZK, Teh I, Nasrallah FA, Teo WP, Ang KK, et al. Brain plasticity following MI-BCI training combined with tDCS in a randomized trial in chronic subcortical stroke subjects: A preliminary study. Sci. Rep. 2017, 7, 9222. DOI: 10.1038/s41598-017-08928-5

[114] Bergmann TO. Brain State-Dependent Brain Stimulation. Front. Psychol. 2018, 9, 2108. DOI: 10.3389/fpsyg.2018.02108

[115] Zrenner C, Desideri D, Belardinelli P, Ziemann U. Real-time EEG-defined excitability states determine efficacy of TMS-induced plasticity in human motor cortex. Brain Stimul. 2018, 11, 374-389. DOI: 10.1016/j.brs.2017.11.016

[116] Jia T, Sun J, McGeady C, Ji L, Li C . Enhancing Brain-Computer Interface Performance by Incorporating Brain-to-Brain Coupling. Cyborg Bionic Syst. 2024, 5, 0116. DOI: 10.34133/cbsystems.0116