The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Restoration of grasp following paralysis through brain-controlled stimulation of muscles
Open accessAbstract
Patients with spinal cord injury lack the connections between brain and spinal cord circuits that are essential for voluntary movement. Clinical systems that achieve muscle contraction through functional electrical stimulation (FES) have proven to be effective in allowing patients with tetraplegia to regain control of hand movements and to achieve a greater measure of independence in daily activities. In existing clinical systems, the patient uses residual proximal limb movements to trigger pre-programmed stimulation that causes the paralysed muscles to contract, allowing use of one or two basic grasps. Instead, we have developed an FES system in primates that is controlled by recordings made from microelectrodes permanently implanted in the brain. We simulated some of the effects of the paralysis caused by C5 or C6 spinal cord injury by injecting rhesus monkeys with a local anaesthetic to block the median and ulnar nerves at the elbow. Then, using recordings from approximately 100 neurons in the motor cortex, we predicted the intended activity of several of the paralysed muscles, and used these predictions to control the intensity of stimulation of the same muscles. This process essentially bypassed the spinal cord, restoring to the monkeys voluntary control of their paralysed muscles. This achievement is a major advance towards similar restoration of hand function in human patients through brain-controlled FES. We anticipate that in human patients, this neuroprosthesis would allow much more flexible and dexterous use of the hand than is possible with existing FES systems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“CNS-based neuroprostheses, or brain–machine interfaces (BMIs), are systems whereby neural electrical activities are decoded and used to control robotic limbs [76, 89, 105] or to stimulate muscles for restoring limb function [35].”
From Implantable neurotechnologies: a review of integrated circuit neural amplifiers (2016) · cites this paper as [35]
In the citing paper
“Currently, all reported BMI experiments with human patients [26, 35, 57, 58, 150] require the subjects tethered percutaneously to a bench-top computer for neural signal acquisition and decoding.”
From Implantable neurotechnologies: a review of integrated circuit neural amplifiers (2016) · cites this paper as [35]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Targeting Recovery: Priorities of the Spinal Cord-Injured Population
Anderson, K. D. (2004) · Journal of Neurotrauma · doi:10.1089/neu.2004.21.1371 · Cited by 2,094
McDonald, J. W., & Sadowsky, C. L. (2002) · The Lancet · doi:10.1016/s0140-6736(02)07603-1 · Cited by 1,858
Learning to control a brain–machine interface for reaching and grasping by primates
Carmena, J. M., Lebedev, M. A., Crist, R. E., O'Doherty, J. E., Santucci, D. M., Dimitrov, D. F., Patil, P. G., Henriquez, C. S., & Nicolelis, M. A. L. (2003) · PLoS Biology · doi:10.1371/journal.pbio.0000042 · Cited by 1,761
Relation of pyramidal tract activity to force exerted during voluntary movement.
Evarts, E. V. (1968) · Journal of Neurophysiology · doi:10.1152/jn.1968.31.1.14 · Cited by 1,258
Direct control of paralysed muscles by cortical neurons
Moritz, C. T., Perlmutter, S. I., & Fetz, E. E. (2008) · Nature · doi:10.1038/nature07418 · Cited by 610
Spasticity after spinal cord injury
Adams, M. M., & Hicks, A. L. (2005) · Spinal Cord · doi:10.1038/sj.sc.3101757 · Cited by 517
Motor and sensory recovery following incomplete tetraplegia
Waters, R. L., Adkins, R. H., Yakura, J. S., & Sie, I. H. (1994) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/0003-9993(94)90034-5 · Cited by 438
Predicting Measures of Motor Performance from Multiple Cortical Spike Trains
Humphrey, D. R., Schmidt, E. M., & Thompson, W. (1970) · Science · doi:10.1126/science.170.3959.758 · Cited by 372
Peckham, P. H., Keith, M. W., Kilgore, K. L., Grill, J. H., Wuolle, K. S., Thrope, G. B., Gorman, P., Hobby, J., Mulcahey, M. J., Carroll, S., et al. (2001) · Archives of Physical Medicine and Rehabilitation · doi:10.1053/apmr.2001.25910 · Cited by 319
Implantable functional neuromuscular stimulation in the tetraplegic hand
Keith, M. W., Peckham, P. H., Thrope, G. B., Stroh, K. C., Smith, B., Buckett, J. R., Kilgore, K. L., & Jatich, J. W. (1989) · The Journal Of Hand Surgery · doi:10.1016/s0363-5023(89)80017-6 · Cited by 212
Flagged first, then by citations
Cited by
Sorted by citations
Items 301–400 of 480
Spinal cord regeneration: A brief overview of the present scenario and a sneak peek into the future
Srivastava, E., Singh, A., & Kumar, A. (2021) · Biotechnology Journal · doi:10.1002/biot.202100167 · Cited by 8
A prefrontal motor circuit initiates persistent movement
Wang, Y., & Sun, Q. (2024) · Nature Communications · doi:10.1038/s41467-024-49615-0 · Cited by 7
Enhanced Performance of Novel Amorphous Silicon Carbide Microelectrode Arrays in Rat Motor Cortex
Haghighi, P., Jeakle, E. N., Sturgill, B. S., Abbott, J., Solis, E. M., Devata, V., Vijayakumar, G., Hernandez-Reynoso, A. G., Cogan, S. F., & Pancrazio, J. J. (2025) · Micromachines · doi:10.3390/mi16020113 · Cited by 7
Optimizing the Usability of Brain-Computer Interfaces
Zhang⋆, Y., & Chase, S. M. (2018) · Neural Computation · doi:10.1162/neco_a_01076 · Cited by 7
Awasthi, P., Lin, T.-H., Bae, J., Miller, L. E., & Danziger, Z. C. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac97c3 · Cited by 7
Self-citationAn Investigation of Manifold-Based Direct Control for a Brain-to-Body Neural Bypass
Losanno, E., Badi, M., Roussinova, E., Bogaard, A. R., Delacombaz, M., Shokur, S., & Micera, S. (2024) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2024.3381475 · Cited by 8
Brain control and information transfer
Tehovnik, E. J., & Chen, L. L. (2015) · Experimental Brain Research · doi:10.1007/s00221-015-4423-5 · Cited by 7
A framework for relating neural activity to freely moving behavior
Foster, J. D., Nuyujukian, P., Freifeld, O., Ryu, S. I., Black, M. J., & Shenoy, K. V. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6346530 · Cited by 7
Neuroprosthetics: Restoring multi-joint motor control
Micera, S. (2017) · Nature Biomedical Engineering · doi:10.1038/s41551-017-0073 · Cited by 7
A novel paraplegia model in awake behaving macaques
Krucoff, M. O., Zhuang, K., MacLeod, D. B., Yin, A. S., Byun, Y. W., Manson, R. J., Turner, D. A., Oliveira, L., & Lebedev, M. (2017) · Journal of Neurophysiology · doi:10.1152/jn.00327.2017 · Cited by 7
Shahdoost, S., Frost, S. B., Guggenmos, D. J., Borrell, J. A., Dunham, C., Barbay, S., Nudo, R. J., & Mohseni, P. (2018) · Analog Integrated Circuits and Signal Processing · doi:10.1007/s10470-017-1093-1 · Cited by 7
Márquez-Chin, C., Kapadia, N. M., & Kalsi‐Ryan, S. (2021) · Synthesis lectures on assistive, rehabilitative, and health-preserving technologies · doi:10.2200/s01090ed1v01y202104arh017 · Cited by 8
Alam, M., & He, J. (2014) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/978-1-4666-6094-6.ch009 · Cited by 7
Pohlmeyer, E. A., Fifer, M. S., Rich, M., Pino, J., Wester, B. A., Johannes, M. S., Dohopolski, C., Helder, J. B., D'Angelo, D., Beaty, J. D., et al. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2263886 · Cited by 8
Phantom Limbs and Brain Plasticity in Amputees
Makin, T. R., & Lab, L. P. (2020) · Oxford University Press eBooks · doi:10.1093/acrefore/9780190264086.013.50 · Cited by 7
Biologically inspired heterogeneous learning for accurate, efficient and low-latency neural network
Wang, B., Zhang, Y., Li, H., Dou, H., Guo, Y., & Deng, Y. (2024) · National Science Review · doi:10.1093/nsr/nwae301 · Cited by 7
Ma, X., Rizzoglio, F., Bodkin, K. L., & Miller, L. E. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/adab93 · Cited by 6
Self-citationHammad, S. H. H., Farina, D., Kamavuako, E. N., & Jensen, W. (2013) · Frontiers in Neuroengineering · doi:10.3389/fneng.2013.00011 · Cited by 6
Obara, K., Kaneshige, M., Suzuki, M., Yokoyama, O., Tazoe, T., & Nishimura, Y. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1127095 · Cited by 6
BCI-Controlled Grasp Neuroprosthesis in High Spinal Cord Injury
Rupp, R., & Muller-Putz, G. R. (2013) · Biosystems & biorobotics · doi:10.1007/978-3-642-34546-3_209 · Cited by 6
Flexible adaptation to an artificial recurrent connection from muscle to peripheral nerve in man
Kato, K. H., Sasada, S., & Nishimura, Y. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00143.2015 · Cited by 6
Triboelectric Nanogenerator for Healthcare
Xiao, X., Yin, J., & Chen, J. (2023) · Springer eBooks · doi:10.1007/978-3-031-28111-2_18 · Cited by 6
Simple decoding of behavior from a complicated neural manifoldPreprint
Perkins, S. M., Cunningham, J. P., Wang, Q., & Churchland, M. M. (2023) · eLife · doi:10.7554/elife.89421.1 · Cited by 6
Neurotechnology in Stroke Rehabilitation
Chaudhary, U. (2025) · Springer eBooks · doi:10.1007/978-3-031-78991-5_2 · Cited by 5
Brain-Computer Interface for Patients with Spinal Cord Injury: A Bibliometric Study
Feng, J., Gao, S., Hu, Y., Sun, G., & Sheng, W. (2024) · World Neurosurgery · doi:10.1016/j.wneu.2024.08.163 · Cited by 5
Population Dynamics in Songbird RA and HVC During Learned Motor-Vocal Behavior
Tostado-Marcos, P., Arneodo, E. M., Ostrowski, L. M., Brown, D. E., Perez, X. A., Kadwory, A., Stanwicks, L. L., Alothman, A., Gentner, T. Q., & Gilja, V. (2026) · Journal of Neuroscience · doi:10.1523/jneurosci.0580-25.2026 · Cited by 6
Improving reaching with functional electrical stimulation by incorporating stiffness modulation
Johnson, T. R., & Taylor, D. M. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac2f7a · Cited by 6
Brain Co-processors: Using AI to Restore and Augment Brain Function
Rao, R. P. N. (2023) · Handbook of Neuroengineering · doi:10.1007/978-981-16-5540-1_32 · Cited by 5
The impact of task context on predicting finger movements in a brain-machine interface
Mender, M. J., Nason, S. R., Temmar, H., Costello, J. T., Wallace, D. M., Willsey, M. S., Kumar, N. G., Kung, T. A., Patil, P. G., & Chestek, C. A. (2023) · eLife · doi:10.7554/elife.82598 · Cited by 7
Wilk, J., Falkowski, P., & Osiak, T. (2024) · Journal of Automation Mobile Robotics & Intelligent Systems · doi:10.14313/jamris/4-2023/27 · Cited by 5
Control of functional electrical stimulation for restoration of motor function
Popović, D. B. (2017) · Facta universitatis - series Electronics and Energetics · doi:10.2298/fuee1703295p · Cited by 5
Advances in BCI: A Neural Bypass Technology to Reconnect the Brain to the Body
Sharma, G., Annetta, N. V., Friedenberg, D. A., & Bockbrader, M. A. (2017) · Springer briefs in electrical and computer engineering · doi:10.1007/978-3-319-64373-1_2 · Cited by 5
Reversing Paralysis Technology - Remarkable Breakthrough for the Treatment of Incurable Paralysis
Hasin, F., Hasan, A. B., Islam, A. R., Hossain, M. M., & Musa, H. T. A. (2018) · International Journal of Human and Technology Interaction (IJHaTI) · Cited by 5
Implications: Societal Collective Outcomes, Including Manufacturing
Cao, J., Meador, M. A., Baba, M. L., Ferreira, P. M., Madou, M. J., Scacchi, W., Spohrer, J., Teague, C., Westmoreland, P. R., & Zhang, X. (2013) · Science policy reports · doi:10.1007/978-3-319-02204-8_7 · Cited by 5
Brain-Machine Interfaces: Restoring and Establishing Communication Channels
Rodenkirch, C., Schriver, B. J., & Wang, Q. (2016) · Springer eBooks · doi:10.1007/978-3-319-31433-4_8 · Cited by 5
Development of wireless neural interface system
Im, C., Koh, C. S., Park, H. Y., Shin, J., Jun, S., Jung, H. H., Ahn, J.-M., Chang, J. W., Kim, Y.-J., & Shin, H.-C. (2016) · Biomedical Engineering Letters · doi:10.1007/s13534-016-0232-4 · Cited by 5
Artificial nerve systems for use in bio-interactive prostheses
Kim, C. H., Seo, D., Lee, Y., & Lee, T. (2025) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-025-00197-z · Cited by 4
Huang, Y., Gong, Y., Liu, Y., & Lü, J.-Q. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.991099 · Cited by 4
Neural Bypasses: Literature Review and Future Directions in Developing Artificial Neural Connections
Zuccaroli, I., Lucke-Wold, B. P., Palla, A., Eremiev, A. N., Sorrentino, Z. A., Zakare-Fagbamila, R. T., McNulty, J., Christie, C., Chandra, V., & Mampre, D. M. (2023) · OBM Neurobiology · doi:10.21926/obm.neurobiol.2301158 · Cited by 4
Surgical Implantation of Brain Computer Interfaces
Khan, S., Anderson, W. D., & Constandinou, T. G. (2023) · JAMA Surgery · doi:10.1001/jamasurg.2023.2399 · Cited by 4
Shenker, B., Fehér, M., Erdősi, P., Papp, L., Vadai, K., Hrivnák, G., Tóth, L., Maróti, P., Horváth, C., & Cserháti, P. (2023) · Acta Polytechnica Hungarica · doi:10.12700/aph.20.8.2023.8.7 · Cited by 4
Deafferented controllers: a fundamental failure mechanism in cortical neuroprosthetic systems
Galán, F., & Baker, S. N. (2015) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2015.00186 · Cited by 4
Tagliabue, M., Francis, N., Hao, Y., Duret, M., Brochier, T. G., Riehle, A., Maier, M. A., & Eskiizmirliler, S. (2015) · International Conference on Advanced Robotics (ICAR) · doi:10.1109/icar.2015.7251473 · Cited by 4
Loutit, A. J., & Potas, J. R. (2020) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2020.00046 · Cited by 4
Intracranialbrain–computer interfaces for communication and control
Jarosiewicz, B., & Hochberg, L. R. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9780511995583.042 · Cited by 4
Brain-Machine Interfaces: From Macro- to Microcircuits
Lebedev, M., & Opriș, I. C. (2015) · Springer eBooks · doi:10.1007/978-94-017-9900-3_21 · Cited by 4
Invasive Brain–Computer Interfaces for Functional Restoration
Ajiboye, A. B., & Kirsch, R. F. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-805353-9.00027-9 · Cited by 7
Rupp, R., Rohm, M., & Schneiders, M. (2014) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/978-1-4666-6094-6.ch012 · Cited by 4
Population subspaces reflect movement intention for arm and brain-machine interface controlPreprint
Lalazar, H., Murray, J. M., Abbott, L. F., & Vaadia, E. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/688259 · Cited by 4
Brain-Controlled Epidural Spinal Stimulation for Upper-Limb Motor Function after TetraplegiaPreprint
Sasaki, A., Fadli, R. A., Yuasa, A., Boogaart, Z., Saito, H., Musikic, N., de Freitas, R., Macellari, N., Davis, K., Datta, N., et al. (2025) · medRxiv · doi:10.1101/2025.07.02.25330433 · Cited by 4
Non-invasive closed-loop spinal stimulation restores leg stepping control in humans with paraplegia
Tazoe, T., Sasada, S., Murayama, T., Nakao, Y., Kato, K., Kadowaki, S., Yoshida, S., Tsuji, H., Ozaki, A., Komiyama, T., et al. (2025) · Brain · doi:10.1093/brain/awaf230 · Cited by 3
Implantable multi-modality microelectrode arrays: Past, present, and future
Wu, B., & Cui, X. T. (2025) · Journal of materials research/Pratt's guide to venture capital sources · doi:10.1557/s43578-025-01736-y · Cited by 3
Cimolato, A., Cekic, D., Katic, N., Razzoli, M., Missiroli, F., Dittli, J., Palopoli, P., Bellomo, S., Kenel, J., Dujović, S. D., et al. (2026) · Science Advances · doi:10.1126/sciadv.ady3144 · Cited by 3
Reaching again: a glimpse of the future with neuroprosthetics
Perlmutter, S. I. (2017) · The Lancet · doi:10.1016/s0140-6736(17)30562-7 · Cited by 3
Hu, D., Wang, S., Li, B., Liu, H., & He, J. (2021) · Brain Sciences · doi:10.3390/brainsci11091193 · Cited by 3
A Review on Neural Amplifier Design for Brain–Machine Interface
Nath, S., Guha, K., & Baishnab, K. L. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-4495-8_28 · Cited by 3
Bouton, C. E. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-805353-9.00029-2 · Cited by 3
Principles of command and control for neuroprostheses
Popović, D. B. (2015) · Elsevier eBooks · doi:10.1016/b978-1-78242-101-6.00003-3 · Cited by 3
Correlation between muscular and nerve signals responsible for hand grasping in non-human primates
Sheshadri, S., Kortelainen, J., Nag, S., Ng, K. A., Bazley, F. A., Michoud, F., Patil, A., Orellana, J., Libedinsky, C., Lahiri, A., et al. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944083 · Cited by 3
Neural Coding for Effective Rehabilitation
Hu, X., Wang, Y., Zhao, T., & Gunduz, A. (2014) · BioMed Research International · doi:10.1155/2014/286505 · Cited by 3
Neurotechnology: a new approach for treating brain disorders.
Robson, J. A., & Davenport, R. J. (2014) · PubMed · Cited by 3
Implantable Neural Sensors for Brain Machine Interface
Jang, J., Lee, J.-H., Kang, M., & Song, Y. (2015) · KAIST research series · doi:10.1007/978-94-017-9981-2_3 · Cited by 3
A Probabilistic Analysis of Muscle Force Uncertainty for Control
Berniker, M., Jarc, A., Kording, K. P., & Tresch, M. C. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2531083 · Cited by 3
Kawase, T., Yoshimura, N., Kambara, H., & Koike, Y. (2016) · Advanced Robotics · doi:10.1080/01691864.2016.1215935 · Cited by 3
Brain-computer interfaces for basic neuroscience
Batista, A. P. (2020) · Handbook of clinical neurology · doi:10.1016/b978-0-444-63934-9.00017-2 · Cited by 3
Hu, K., Bounni, F., & Williams, Z. M. (2017) · Neurosurgical FOCUS · doi:10.3171/2017.5.focus17244 · Cited by 3
Kasten, M. R., Ievins, A., & Moritz, C. T. (2015) · Wiley eBooks · doi:10.1002/9780470015902.a0024011 · Cited by 3
A scalable bonding technique for the development of next-generation brain-machine interfaces
Wang, P., Goh, T., Hemed, N., & Melosh, N. (2019) · 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) · doi:10.1109/ner.2019.8716886 · Cited by 2
Digital Interface Brain Computer Interaction Method Based on Icon Control
Niu, Y., Xue, C., Wang, H., Tang, W., Zhang, X., Jin, T., & Chen, Y. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-58625-0_27 · Cited by 2
Johnson, T. R., Haddix, C. A., Ajiboye, A. B., & Taylor, D. M. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/adc9e3 · Cited by 2
Neural decoding and feature selection methods for closed-loop control of avoidance behavior
Liu, J., Younk, R., Drahos, L. M., Nagrale, S. S., Yadav, S., Widge, A. S., & Shoaran, M. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad8839 · Cited by 2
Few-shot Algorithms for Consistent Neural Decoding (FALCON) Benchmark
Karpowicz, B. M., Ye, J., Fan, C., Tostado-Marcos, P., Rizzoglio, F., Washington, C., Scodeler, T., de Lucena, D., Nason, S. R., Mender, M. J., et al. (2024) · neural information processing systems · doi:10.52202/079017-2439 · Cited by 6
Self-citationChang, Y. H., Chen, M., Shanechi, M. M., Carmena, J. M., & Tomlin, C. J. (2014) · European Control Conference (ECC) · doi:10.1109/ecc.2014.6862547 · Cited by 2
Brain Computer Interface for Spinal Cord Injury
Long, J. (2014) · Brain Disorders & Therapy · doi:10.4172/2168-975x.1000e116 · Cited by 2
Reinforcement Learning for Hand Grasp with Surface Multi-field Neuroprostheses
Ojanguren, E. I., Irigoyen, E., & Keller, T. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-47364-2_30 · Cited by 2
Shahdoost, S., Nudo, R. J., & Mohseni, P. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2615270 · Cited by 2
Neural decoding with SVM and feature selection in a rat active tactile discrimination task
Gajadhar, A., Moioli, R. C., Melo, B. K. A. D. S., Kunicki, A. C. B., Peres, A. S. C., & do Rego, T. G. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489474 · Cited by 2
In the Spotlight: Neuroengineering
Thakor, N. V. (2012) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2012.2228515 · Cited by 2
Rupp, R., Rohm, M., & Schneiders, M. (2017) · IGI Global eBooks · doi:10.4018/978-1-5225-3158-6.ch036 · Cited by 2
Mihaylova, Y., Pozdeeva, A., Leukhin, A., Toschev, A. S., Talanov, M. O., Vallverdú, J., & Suleimanova, A. (2022) · Journal of Artificial Intelligence and Consciousness · doi:10.1142/s270507852230002x · Cited by 2
Brain-Computer Interface Through the Prism of Modern Age
Radončić, A., Hadžić, S., & Laković, J. (2024) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-49068-2_32 · Cited by 2
Shanechi, M. M. (2017) · Springer eBooks · doi:10.1007/978-3-319-71976-4_8 · Cited by 2
Li, Y., Zhang, J., Zhang, C., Xiao, Y., Hong, J., Wang, M. Y., & Li, Y. (2018) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/urai.2018.8441816 · Cited by 2
Reinforcement schedules differentially affect learning in neuronal operant conditioning in rats
Song, K., TAKAHASHI, S. Y., & Sakurai, Y. (2019) · Neuroscience Research · doi:10.1016/j.neures.2019.04.003 · Cited by 2
Effect of VMD decomposition of soleus muscle EMG in SVM classification
Akhil, V. M., Varghese, J., Rajendrakumar, P. K., & Sivanandan, K. S. (2019) · International Conference on Advanced Computational and Communication Paradigms (ICACCP) · doi:10.1109/icaccp.2019.8882900 · Cited by 2
Faisal, A. A. (2021) · Science · doi:10.1126/science.abi7262 · Cited by 2
Barra, B., Rose, D. S., Kumar, R., Gopinath, C., Mirzakhalili, E., Lempka, S. F., Gaunt, R. A., Głowacki, E. D., & Fisher, L. E. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.14.580219 · Cited by 2
Advancing Spine Connectomics and Neural Integration through Machine Learning and NeuroengineeringPreprint
Kumar, R. K., Sporn, K., Gowda, C., Khanna, A., Prabhakar, P., Paladugu, P., Jagadeesan, R., Clarkson, L., Chandrahasegowda, S., Kumar, T., et al. (2025) · Preprints.org · doi:10.20944/preprints202506.0518.v1 · Cited by 2
Eyvazpour, R., Farrokhi, B., & Erfanian, A. (2025) · iScience · doi:10.1016/j.isci.2025.114521 · Cited by 2
New Applications of Neurotechnologies for Neurological Disorders
Orsborn, A. L., Pirondini, E., Capogrosso, M., Gaunt, R. A., & Stavisky, S. D. (2025) · Springer eBooks · doi:10.1007/978-3-031-89307-0_25 · Cited by 1
Rethink the motor cortical control via the experiment-analysis-model flywheel: an overview
Jiang, H., Bu, X., Shi, Q., Zheng, Z., Zhu, D., Tang, H., & Chen, Y. (2025) · Med-X · doi:10.1007/s44258-025-00059-1 · Cited by 1
Bioelectronic medicine: a new frontier for autonomic nervous system disorders
Dhall, M., Tushir, R., Sharma, P., Rani, N., & Singh, T. G. (2025) · Epilepsy and Paroxysmal Conditions · doi:10.17749/2077-8333/epi.par.con.2025.231 · Cited by 1
Design and characterization of on-head antenna systems for neural motor prosthesis
See, T. S. P., Qing, X., & Chen, Z. N. (2015) · European Conference on Antennas and Propagation · Cited by 1
Hauptman, J., & Everson, R. (2012) · Surgical Neurology International · doi:10.4103/2152-7806.101002 · Cited by 1
Human Single Unit Activity for Reach and Grasp Motor Prostheses
Fried, I. (2014) · The MIT Press eBooks · doi:10.7551/mitpress/9780262027205.003.0017 · Cited by 1
Malik, W. Q., & Ajemian, R. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-800454-8.00001-x · Cited by 1
Hybrid hermetic housings for active implantable neural device
Cheng, M.-Y., Chen, W., Lim, R., & Damalerio, R. (2017) · IEEE Electronics Packaging Technology Conference (EPTC) · doi:10.1109/eptc.2017.8277483 · Cited by 1
Restoring upper extremity function with brain-machine interfaces
Nason, S. R., Mender, M. J., Letner, J. G., Chestek, C. A., & Patil, P. G. (2021) · International review of neurobiology · doi:10.1016/bs.irn.2021.06.001 · Cited by 2
Wang, R., Yu, H., & Li, Z. (2017) · Toxinology · doi:10.1007/978-981-10-2798-7_41-1 · Cited by 2
Triboelectric Nanogenerator for Healthcare
Xiao, X., Yin, J., & Chen, J. (2022) · Springer eBooks · doi:10.1007/978-3-031-05722-9_18-1 · Cited by 1
Metadata
- Authors
- Ethier, C., Oby, E. R., Bauman, M. J., & Miller, L. E.
- Affiliations
- Northwestern University (US)
- University of Pittsburgh (US)
- Corresponding author
- Miller, L. E.
- Venue
- Nature
- Published
- 2012-04-18
- Volume
- 485
- Issue
- 7398
- Pages
- 368-371
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3358575
- OpenAlex
- W1988067967
- Cited by
- 485
- References
- 26
- Citations by year
- 2026: 9 · 2025: 22 · 2024: 24 · 2023: 27 · 2022: 24 · 2021: 33 · 2020: 29 · 2019: 28 · 2018: 42 · 2017: 44 · 2016: 46 · 2015: 38 · 2014: 55 · 2013: 46 · 2012: 18
- FWCI
- 20.69
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS053603, F31NS071737, #NS053603)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Health Sciences › Medicine › Clinical Neurology › Spinal Cord Injury Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- brain-computer interface, functional electrical stimulation, paralysis, spinal cord injury, motor cortex, neural prostheses, hand grasping, rhesus monkeys, tetraplegia, hand function recovery, prosthetic hand control
- MeSH terms
- Animals, Electric Stimulation, Electrodes, Implanted, Hand, Macaca mulatta, Microelectrodes, Motor Cortex, Movement, Muscles, Nerve Block, Paralysis, Quadriplegia, Spinal Cord Injuries, Hand Strength, Prostheses and Implants
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review