Advances in Bidirectional Neural Interaction for Intelligent Upper-Limb Prostheses

Abstract

Bidirectional neural interaction pathways play a critical role in determining the performance of intelligent upper-limb prostheses. Specifically, the nervous system should be able to control prosthetic movements according to the user’s intention, while the operating state of the prosthesis should be conveyed back to the user through sensory feedback interfaces, thereby establishing a bidirectional interface between the prosthesis and the human nervous system. This paper introduces the major approaches for neural motor control, including brain–computer interfaces and myoelectric interfaces, discusses stimulation modalities and sensory mapping strategies for sensory feedback, and analyzes future directions for bidirectional sensorimotor interfaces in upper-limb prostheses.

Keywords

Upper-limb prosthesis / Bidirectional neural interface / Neuromotor interface / Somatosensory feedback / Human-machine interaction

References

[1] Wang H, Zhang Y, Karrenbach M, Ding Y, He B. Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control. Nat. Commun. 2026, 17, 6177. DOI: 10.1038/s41467-026-75435-5

[2] CTRL-labs at Reality Labs, Sussillo D, Kaifosh P, Reardon T . A generic noninvasive neuromotor interface for human-computer interaction. Biorxiv 2024, 2024-02. DOI: 10.1101/2024.02.23.581779

[3] Yu Y, Chen C, Zhao J, Sheng X, Zhu X. Surface Electromyography Image-Driven Torque Estimation of Multi-DoF Wrist Movements. IEEE Trans. Ind. Electron. 2022, 69, 795-804. DOI: 10.1109/TIE.2021.3050367

[4] Hajian G,Morin E. Deep Multi-Scale Fusion of Convolutional Neural Networks for EMG-Based Movement Estimation. IEEE Trans. Neural Syst. Rehabil. Eng. 2022, 30, 486-495. DOI: 10.1109/TNSRE.2022.3153252

[5] Grison A, Mendez Guerra I, Clarke AK, Muceli S, Ibáñez J, Farina D . Unlocking the full potential of high-density surface EMG: Novel non-invasive high-yield motor unit decomposition. J. Physiol. 2025, 603, 2281-2300. DOI: 10.1113/JP287913

[6] Panchal M, Tanzarella S, Jung MK, Farina D. Mapping Intrinsic and Extrinsic Muscle Myoelectric Activity During Natural Dynamic Movements Into Finger and Wrist Kinematics Using Deep Learning Prediction Models. IEEE Trans. Hum.-Mach. Syst. 2023, 53, 924-934. DOI: 10.1109/THMS.2023.3302613

[7] Lin C, Zhao C, Zhang J, Chen C, Jiang N, Farina D, et al. Continuous Estimation of Hand Kinematics From Electromyographic Signals Based on Power-and Time-Efficient Transformer Deep Learning Network. IEEE Trans. Neural Syst. Rehabil. Eng. 2025, 33, 58-67. DOI: 10.1109/TNSRE.2024.3514938

[8] Deng H, Wei Z, Hu X, Zeng H, Song A, Zhang D, et al. Online Unsupervised Adaptation of Latent Representation for Myoelectric Control During User-Decoder Co-Adaptation. IEEE Trans. Neural Syst. Rehabil. Eng. 2025, 33, 1026-1037. DOI: 10.1109/TNSRE.2025.3545818

[9] Gholinezhad S, Farina D, Dosen S, Dideriksen J. Encoding force modulation in two electrotactile feedback parameters strengthens sensory integration according to maximum likelihood estimation. Sci. Rep. 2023, 13, 12461. DOI: 10.1038/s41598-023-38753-y

[10] Iberite F, Muheim J, Akouissi O, Gallo S, Rognini G, Morosato F, et al. Restoration of natural thermal sensation in upper-limb amputees. Science 2023, 380, 731-735. DOI: 10.1126/science.adf6121

[11] Osborn LE, Venkatasubramanian R, Himmtann M, Moran CW, Pierce JM, Gajendiran P, et al. Evoking natural thermal perceptions using a thin-film thermoelectric device with high cooling power density and speed. Nat. Biomed. Eng. 2023, 8, 1004-1017. DOI: 10.1038/s41551-023-01070-w

[12] Marasco PD, Hebert JS, Sensinger JW, Shell CE, Schofield JS, Thumser ZC, et al. Illusory movement perception improves motor control for prosthetic hands. Sci. Transl. Med. 2018, 10, eaao6990. DOI: 10.1126/scitranslmed.aao6990

[13] Shokur S, Bassolino M, Kang N, Ackerley R, Micera S . Hand prostheses with somatosensory feedback: Functional and clinical benefits. Lancet Neurol. 2026, 25, 421-432. DOI: 10.1016/S1474-4422(25)00493-4

[14] Lecompte O, Achiche S, Mohebbi A . A Review of Proprioceptive Feedback Strategies for Upper-Limb Myoelectric Prostheses. IEEE Trans. Med. Robot. Bionics 2024, 6, 930-939. DOI: 10.1109/TMRB.2024.3407532

[15] Donati E, Valle G. Neuromorphic hardware for somatosensory neuroprostheses. Nat. Commun. 2024, 15, 556. DOI: 10.1038/s41467-024-44723-3

[16] Zhang J, Chou C-H, Hao M, Liang W, Zhang Z, Xie A, et al. Somatotopically Evoked Tactile Sensation via Transcutaneous Electrical Nerve Stimulation Improves Prosthetic Sensorimotor Performance. IEEE Trans. Neural Syst. Rehabil. Eng. 2024, 32, 2815-2825. DOI: 10.1109/TNSRE.2024.3435570

[17] Bockbrader M.Upper limb sensorimotor restoration through brain-computer interface technology in tetraparesis. Curr. Opin. Biomed. Eng. 2019, 11, 85-101. DOI: 10.1016/j.cobme.2019.09.002

[18] Chandrasekaran S, Wandelt SK, Jangam A, Elias Z, Ibroci E, Maffei C, et al. A neuroprosthesis for restoring hand movement and sensation in a person with complete tetraplegia. Nat. Med. 2026, 32, 2591-2601. DOI: 10.1038/s41591-026-04498-0

[19] Liu X-Y, Wang W-L, Liu M, Chen M-Y, Pereira T, Doda DY, et al. Recent applications of EEG-based brain-computer-interface in the medical field. Mil. Med. Res. 2025, 12, 14. DOI: 10.1186/s40779-025-00598-z

[20] Patrick-Krueger KM, Burkhart I, Contreras-Vidal JL . The state of clinical trials of implantable brain-computer interfaces. Nat. Rev. Bioeng. 2024, 3, 50-67. DOI: 10.1038/s44222-024-00239-5

[21] Hameed I, Khan DM, Ahmed SM, Aftab SS, Fazal H. Enhancing motor imagery EEG signal decoding through machine learning: A systematic review of recent progress. Comput. Biol. Med. 2025, 185, 109534. DOI: 10.1016/j.compbiomed.2024.109534

[22] Edelman BJ, Zhang S, Schalk G, Brunner P, Müller-Putz G, Guan C, et al. Non-Invasive Brain-Computer Interfaces: State of the Art and Trends. IEEE Rev. Biomed. Eng. 2025, 18, 26-49. DOI: 10.1109/RBME.2024.3449790

[23] Huang Q, Zhang Z, Yu T, He S, Li Y. An EEG-/EOG-Based Hybrid Brain-Computer Interface: Application on Controlling an Integrated Wheelchair Robotic Arm System. Front. Neurosci. 2019, 13, 1243. DOI: 10.3389/fnins.2019.01243

[24] Hong J, Wang W, Najafizadeh L. ChatBCI, a P300 speller BCI with context-driven word prediction leveraging large language models, from concept to evaluation. Sci. Rep. 2026, 16, 6379. DOI: 10.1038/s41598-025-25660-7

[25] Kalra J, Mittal P, Mittal N, Arora A, Tewari U, Chharia A, et al. How Visual Stimuli Evoked P300 is Transforming the Brain-Computer Interface Landscape: A PRISMA Compliant Systematic Review. IEEE Trans. Neural Syst. Rehabil. Eng. 2023, 31, 1429-1439. DOI: 10.1109/TNSRE.2023.3246588

[26] Tang J, Xu M, Han J, Liu M, Dai T, Chen S, et al. Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling. Sensors 2020, 20, 4186. DOI: 10.3390/s20154186

[27] Muller-Putz GR, Pfurtscheller G . Control of an Electrical Prosthesis With an SSVEP-Based BCI. IEEE Trans. Biomed. Eng. 2008, 55, 361-364. DOI: 10.1109/TBME.2007.897815

[28] Albán-Escobar M, Navarrete-Arroyo P, De La Cruz-Guevara DR, Tobar-Quevedo J . Assistance Device Based on SSVEP-BCI Online to Control a 6-DOF Robotic Arm. Sensors 2024, 24, 1922. DOI: 10.3390/s24061922

[29] Cheng L, Li W, Liang L, Zhou J, Zhang Q. Hybrid BCI robotic arm control system based on MI and SSVEP. Biomed. Signal Process. Control 2025, 104, 107477. DOI: 10.1016/j.bspc.2024.107477

[30] Yu Y, Liu Y, Jiang J, Yin E, Zhou Z,Hu D. An Asynchronous Control Paradigm Based on Sequential Motor Imagery and Its Application in Wheelchair Navigation. IEEE Trans. Neural Syst. Rehabil. Eng. 2018, 26, 2367-2375. DOI: 10.1109/TNSRE.2018.2881215

[31] Tonin L, Bauer FC, Del R. Millan J. The Role of the Control Framework for Continuous Teleoperation of a Brain-Machine Interface-Driven Mobile Robot. IEEE Trans. Robot. 2020, 36, 78-91. DOI: 10.1109/TRO.2019.2943072

[32] Al-Saegh A, Dawwd SA, Abdul-Jabbar JM . Deep learning for motor imagery EEG-based classification: A review. Biomed. Signal Process. Control 2021, 63, 102172. DOI: 10.1016/j.bspc.2020.102172

[33] Zhang H, Zhou Q-Q, Chen H, Hu X-Q, Li W-G, Bai Y, et al. The applied principles of EEG analysis methods in neuroscience and clinical neurology. Mil. Med. Res. 2023, 10, 67. DOI: 10.1186/s40779-023-00502-7

[34] Jiang X, Meng L, Chen X, Wu D. A CSP-based retraining framework for motor imagery based brain-computer interfaces. Sci. China Inf. Sci. 2024, 67, 189403. DOI: 10.1007/s11432-024-4081-x

[35] Lotte F, Bougrain L, Cichocki A, Clerc M, Congedo M, Rakotomamonjy A, et al. A review of classification algorithms for EEG-based brain-computer interfaces: A 10 year update. J. Neural Eng. 2018, 15, 031005. DOI: 10.1088/1741-2552/aab2f2

[36] Song T, Zheng W, Song P, Cui Z. EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks. IEEE Trans. Affect. Comput. 2020, 11, 532-541. DOI: 10.1109/TAFFC.2018.2817622

[37] Yao Y, Wang H, Chen L, Peng Y,Luo J. Foundation models for EEG decoding: current progress and prospective research. J. Neural Eng. 2025, 22, 061002. DOI: 10.1088/1741-2552/ae17e9

[38] Wang T, Yao S, Shao L-H, Zhu Y. Stretchable Ag/AgCl Nanowire Dry Electrodes for High-Quality Multimodal Bioelectronic Sensing. Sensors 2024, 24, 6670. DOI: 10.3390/s24206670

[39] Rolandino G, Zangrandi C, Vieira T, Luigi Cerone G, Andrews B, FitzGerald JJ . HDE-Array: Development and Validation of a New Dry Electrode Array Design to Acquire HD-sEMG for Hand Position Estimation. IEEE Trans. Neural Syst. Rehabil. Eng. 2024, 32, 4004-4013. DOI: 10.1109/TNSRE.2024.3490796

[40] Hudgins B, Parker P, Scott RN. A new strategy for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 1993, 40, 82-94. DOI: 10.1109/10.204774

[41] Tkach D, Huang H, Kuiken TA. Study of stability of time-domain features for electromyographic pattern recognition. J. NeuroEng. Rehabil. 2010, 7, 21. DOI: 10.1186/1743-0003-7-21

[42] Phinyomark A, Phukpattaranont P, Limsakul C. Feature reduction and selection for EMG signal classification. Expert Syst. Appl. 2012, 39, 7420-7431. DOI: 10.1016/j.eswa.2012.01.102

[43] Yang D, Liu H. An EMG-Based Deep Learning Approach for Multi-DOF Wrist Movement Decoding. IEEE Trans. Ind. Electron. 2022, 69, 7099-7108. DOI: 10.1109/TIE.2021.3097666

[44] Guo W, Jiang N, Farina D, Su J, Wang Z, Lin C, et al. Multi-Attention Feature Fusion Network for Accurate Estimation of Finger Kinematics From Surface Electromyographic Signals. IEEE Trans. Hum.-Mach. Syst. 2023, 53, 512-519. DOI: 10.1109/THMS.2023.3252817

[45] Shin Y, Kim J, Choi S-I. Palm sEMG-based user identification during doorknob rotation using a convolutional neural network. Sci. Rep. 2026, 16, 22244. DOI: 10.1038/s41598-026-46294-3

[46] Holobar A, Zazula D. Multichannel Blind Source Separation Using Convolution Kernel Compensation. IEEE Trans. Signal Process. 2007, 55, 4487-4496. DOI: 10.1109/TSP.2007.896108

[47] Negro F, Muceli S, Castronovo AM, Holobar A, Farina D. Multi-channel intramuscular and surface EMG decomposition by convolutive blind source separation. J. Neural Eng. 2016, 13, 026027. DOI: 10.1088/1741-2560/13/2/026027

[48] Yu Y, Zhou Z, Xu Y, Chen C, Guo W, Sheng X. Toward Hand Gesture Recognition Using a Channel-Wise Cumulative Spike Train Image-Driven Model. Cyborg Bionic Syst. 2025, 6, 0219. DOI: 10.34133/cbsystems.0219

[49] Li D, Chen C, Zhu K, Guo R, Shull PB. Integrating Motor Unit Activity With Deep Learning for Real-Time, Simultaneous and Proportional Wrist Angle and Grasp Force Estimation. IEEE Trans. Biomed. Eng. 2025, 72, 3682-3693. DOI: 10.1109/TBME.2025.3575252

[50] Meng L, Hu X. Motor unit-driven decoders for long-term motion intention: Effects of training strategies. Biomed. Signal Process. Control 2026, 114, 109310. DOI: 10.1016/j.bspc.2025.109310

[51] Li D, Liu Y, Chen X, Zhou P, Zhang X. Real-time decoding fine motor intentions from a set of motor unit activities using set transformer. Biomed. Signal Process. Control 2026, 117, 109597. DOI: 10.1016/j.bspc.2026.109597

[52] Cady SR, Lambrecht JM, Dsouza KT, Dunning JL, Anderson JR, Malone KJ, et al. First-in-human implementation of a bidirectional somatosensory neuroprosthetic system with wireless communication. J. NeuroEng. Rehabil. 2025, 22, 90. DOI: 10.1186/s12984-025-01613-z

[53] Chai G, Sui X, Li S, He L,Lan N. Characterization of evoked tactile sensation in forearm amputees with transcutaneous electrical nerve stimulation. J. Neural Eng. 2015, 12, 066002. DOI: 10.1088/1741-2560/12/6/066002

[54] D’Anna E, Petrini FM, Artoni F, Popovic I, Simanić I, Raspopovic S, et al. A somatotopic bidirectional hand prosthesis with transcutaneous electrical nerve stimulation based sensory feedback. Sci. Rep. 2017, 7, 10930. DOI: 10.1038/s41598-017-11306-w

[55] Fontana E, Natili G, Catalano MG, Grioli G, Bicchi A. Exploring transcutaneous electrostimulation for proprioceptive feedback in upper limb prostheses: A pilot study. J. NeuroEng. Rehabil. 2025, 22, 242. DOI: 10.1186/s12984-025-01772-z

[56] Kharboush R, Valdunciel AP, Boesendorfer A, Baumgartner B, Aszmann OC, Pereda JI, et al. Transcutaneous Spinal Cord Stimulation Provides Sensations to the Missing Hand of Individuals with Upper Limb Amputation. IEEE Trans. Neural Syst. Rehabil. Eng. 2026, 34, 1119-1130. DOI: 10.1109/TNSRE.2026.3661849

[57] Pylatiuk C, Kargov A, Schulz S. Design and Evaluation of a Low-Cost Force Feedback System for Myoelectric Prosthetic Hands. JPO J. Prosthet. Orthot. 2006, 18, 57-61. DOI: 10.1097/00008526-200604000-00007

[58] Wei Z, Song A, Guo F, Dosen S, Hu X, Zhao Z, et al. A Multimodal Stimulation System for Conveying Diverse Feedback in Hand Prosthetics: Preliminary Assessment. IEEE Trans. Neural Syst. Rehabil. Eng. 2026, 34, 57-68. DOI: 10.1109/TNSRE.2025.3636435

[59] Wu G, Gu W, Luo Y, Zhang X, Li L, Hou J,et al.Study on Prosthetic Hand Proprioception Feedback Based on Hybrid Vibro-Electrotactile Stimulation. IEEE Trans. Neural Syst. Rehabil. Eng. 2025, 33, 2967-2976. DOI: 10.1109/TNSRE.2025.3593354

[60] Leskovar R, Kyberd PJ, Moore JM, Exell TA, Ostler C. The proprioception illusion can simulate limb movement in persons with limb difference. Sci. Rep. 2026, 16, 3897. DOI: 10.1038/s41598-025-30687-x

[61] Antfolk C, D’Alonzo M, Controzzi M, Lundborg G, Rosen B, Sebelius F, et al. Artificial Redirection of Sensation from Prosthetic Fingers to the Phantom Hand Map on Transradial Amputees: Vibrotactile Versus Mechanotactile Sensory Feedback. IEEE Trans. Neural Syst. Rehabil. Eng. 2013, 21, 112-120. DOI: 10.1109/TNSRE.2012.2217989

[62] Huaroto JJ, Suarez E, Krebs HI, Marasco PD, Vela EA. A Soft Pneumatic Actuator as a Haptic Wearable Device for Upper Limb Amputees: Toward a Soft Robotic Liner. IEEE Robot. Autom. Lett. 2019, 4, 17-24. DOI: 10.1109/LRA.2018.2874379

[63] Barontini F, Catalano MG, Grioli G, Bianchi M, Bicchi A. Wearable Integrated Soft Haptics in a Prosthetic Socket. IEEE Robot. Autom. Lett. 2021, 6, 1785-1792. DOI: 10.1109/LRA.2021.3060432

[64] Abd MA, Ingicco J, Hutchinson DT, Tognoli E, Engeberg ED. Multichannel haptic feedback unlocks prosthetic hand dexterity. Sci. Rep. 2022, 12, 2323. DOI: 10.1038/s41598-022-04953-1

[65] Colella N, Bianchi M, Grioli G, Bicchi A, Catalano MG. A Novel Skin-Stretch Haptic Device for Intuitive Control of Robotic Prostheses and Avatars. IEEE Robot. Autom. Lett. 2019, 4, 1572-1579. DOI: 10.1109/LRA.2019.2896484

[66] Ploumitsakou M, Muheim J, Felouzis A, Carbonell Muñoz N-I, Iberite F, Akouissi O, et al. Remapping Wetness Perception in Upper Limb Amputees. Adv. Intell. Syst. 2024, 6, 2300512. DOI: 10.1002/aisy.202300512

[67] Zhang J, Hao M, Yang F, Liang W, Sun A, Chou C-H, et al. Evaluation of multiple perceptual qualities of transcutaneous electrical nerve stimulation for evoked tactile sensation in forearm amputees. J. Neural Eng. 2022, 19, 026041. DOI: 10.1088/1741-2552/ac6062

[68] Dumanian GA, Potter BK, Mioton LM, Ko JH, Cheesborough JE, Souza JM, et al. Targeted Muscle Reinnervation Treats Neuroma and Phantom Pain in Major Limb Amputees A Randomized Clinical Trial. Ann. Surg. 2019, 270, 238-246. DOI: 10.1097/SLA.0000000000003088

[69] Gardetto A, Müller-Putz GR, Eberlin KR, Bassetto F, Atkins DJ, Turri M, et al. Restoration of Genuine Sensation and Proprioception of Individual Fingers Following Transradial Amputation with Targeted Sensory Reinnervation as a Mechanoneural Interface. J. Clin. Med. 2025, 14, 417. DOI: 10.3390/jcm14020417

[70] D’Anna E, Valle G, Mazzoni A, Strauss I, Iberite F, Patton J, et al. A closed-loop hand prosthesis with simultaneous intraneural tactile and position feedback. Sci. Robot. 2019, 4, eaau8892. DOI: 10.1126/scirobotics.aau8892

[71] Flesher SN, Downey JE, Weiss JM, Hughes CL, Herrera AJ, Tyler-Kabara EC, et al. A brain-computer interface that evokes tactile sensations improves robotic arm control. Science 2021, 372, 831-836. DOI: 10.1126/science.abd0380

[72] Sujatha Ravindran A, Contreras-Vidal J . An empirical comparison of deep learning explainability approaches for EEG using simulated ground truth. Sci. Rep. 2023, 13, 17709. DOI: 10.1038/s41598-023-43871-8

[73] Rajpura P, Cecotti H, Kumar Meena Y. Explainable artificial intelligence approaches for brain-computer interfaces: A review and design space. J. Neural Eng. 2024, 21, 041003. DOI: 10.1088/1741-2552/ad6593

[74] Zhao Y, Zhang Z, Li Z, Yang Z, Dehghani-Sanij AA, Xie S. An EMG-Driven Musculoskeletal Model for Estimating Continuous Wrist Motion. IEEE Trans. Neural Syst. Rehabil. Eng. 2020, 28, 3113-3120. DOI: 10.1109/TNSRE.2020.3038051

[75] Han J, Ding Q, Xiong A, Zhao X. A State-Space EMG Model for the Estimation of Continuous Joint Movements. IEEE Trans. Ind. Electron. 2015, 62, 4267-4275. DOI: 10.1109/TIE.2014.2387337

[76] Zhou H,Alici G. Non-Invasive Human-Machine Interface (HMI) Systems With Hybrid On-Body Sensors for Controlling Upper-Limb Prosthesis: A Review. IEEE Sens. J. 2022, 22, 10292-10307. DOI: 10.1109/JSEN.2022.3169492

[77] Li X, Samuel OW, Zhang X, Wang H, Fang P, Li G. A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees. J. NeuroEng. Rehabil. 2017, 14, 2. DOI: 10.1186/s12984-016-0212-z

[78] Valle G. Peripheral neurostimulation for encoding artificial somatosensations. Eur. J. Neurosci. 2022, 56, 5888-5901. DOI: 10.1111/ejn.15822

[79] Valle G, Katic Secerovic N, Eggemann D, Gorskii O, Pavlova N, Petrini FM,et al.Biomimetic computer-to-brain communication enhancing naturalistic touch sensations via peripheral nerve stimulation. Nat. Commun. 2024, 15, 1151. DOI: 10.1038/s41467-024-45190-6

[80] Wei Y, Marshall AG, McGlone FP, Makdani A, Zhu Y, Yan L, et al. Human tactile sensing and sensorimotor mechanism: From afferent tactile signals to efferent motor control. Nat. Commun. 2024, 15, 6857. DOI: 10.1038/s41467-024-50616-2

[81] Chen L, Karilanova S, Chaki S, Wen C, Wang L, Winblad B, et al. Spike timing-based coding in neuromimetic tactile system enables dynamic object classification. Science 2024, 384, 660-665. DOI: 10.1126/science.adf3708

[82] Valle G,Petrini FM, Strauss I, Iberite F, D’Anna E, Granata G, et al. Comparison of linear frequency and amplitude modulation for intraneural sensory feedback in bidirectional hand prostheses. Sci. Rep. 2018, 8, 16666. DOI: 10.1038/s41598-018-34910-w

[83] Geng B, Dong J, Jensen W, Dosen S, Farina D, Kamavuako EN. Psychophysical Evaluation of Subdermal Electrical Stimulation in Relation to Prosthesis Sensory Feedback. IEEE Trans. Neural Syst. Rehabil. Eng. 2018, 26, 709-715. DOI: 10.1109/TNSRE.2018.2803844

[84] Guémann M, Halgand C, Bastier A, Lansade C, Borrini L, Lapeyre É, et al. Sensory substitution of elbow proprioception to improve myoelectric control of upper limb prosthesis: experiment on healthy subjects and amputees. J. NeuroEng. Rehabil. 2022, 19, 59. DOI: 10.1186/s12984-022-01038-y

[85] Blustein D, Wilson A, Sensinger J. Assessing the quality of supplementary sensory feedback using the crossmodal congruency task. Sci. Rep. 2018, 8, 6203. DOI: 10.1038/s41598-018-24560-3

[86] Su S, Chai G, Meng J, Sheng X, Mouraux A,Zhu X. Towards optimizing the non-invasive sensory feedback interfaces in a neural prosthetic control. J. Neural Eng. 2022, 19, 016028. DOI: 10.1088/1741-2552/ac4e1b

[87] Wang Y, Fang P, Tang X, Jiang N, Tian L, Li X, et al. Effective Evaluation of Finger Sensation Evoking by Non-Invasive Stimulation for Sensory Function Recovery in Transradial Amputees. IEEE Trans. Neural Syst. Rehabil. Eng. 2022, 30, 519-528. DOI: 10.1109/TNSRE.2022.3155756