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Reach and grasp by people with tetraplegia using a neurally controlled robotic arm
Open accessAbstract
Paralysis following spinal cord injury, brainstem stroke, amyotrophic lateral sclerosis and other disorders can disconnect the brain from the body, eliminating the ability to perform volitional movements. A neural interface system could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with long-standing tetraplegia can use a neural interface system to move and click a computer cursor and to control physical devices. Able-bodied monkeys have used a neural interface system to control a robotic arm, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here we demonstrate the ability of two people with long-standing tetraplegia to use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm and hand over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor 5 years earlier, also used a robotic arm to drink coffee from a bottle. Although robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after injury to the central nervous system, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.
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“The flexibility of ConvNets might also be a limitation in some brain‐signal decoding scenarios. For example if the user of a brain–computer interface should learn to adapt her or his brain signals to the decoding model, a simpler feature‐based model might yield better results.”
From Deep learning with convolutional neural networks for EEG decoding and visualization (2017) · cites this paper as Hochberg et al., 2012
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“Subcutaneous invasive electrophysiological signals can be derived through brain interfaces,[270–272] using electrocorticography (ECoG)[273,274] on the cortical surface as well as local field potentials (LFP)[275,276] extracted from electrodes inserted into the cortex.”
From Flexible Electronics and Devices as Human–Machine Interfaces for Medical Robotics (2022) · cites this paper as [275]
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“Prior BCI studies have shown that the motor intention for gross motor skills, such as reaching, grasping or moving a computer cursor, remains neurally encoded in motor cortex after paralysis1–7.”
From High-performance brain-to-text communication via handwriting (2021) · cites this paper as [1]
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“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 [58]
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“Additionally, it is apparent from previous work that access to sufficiently large patient populations for concrete and statistically significant conclusions may be difficult to obtain (1–5, 8, 11, 29, 39).”
From Noninvasive neuroimaging enhances continuous neural tracking for robotic device control (2019) · cites this paper as [39]
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“The results presented here demonstrate that CP control provides a unique opportunity for the complex control of a virtual cursor and robotic device (14, 39), without requiring discretized, prolonged task sequences (46) that can make even simple task completion long and frustrating.”
From Noninvasive neuroimaging enhances continuous neural tracking for robotic device control (2019) · cites this paper as [39]
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“Highcount microelectrode arrays have proven useful in basic physiology research [8], and for clinical applications such as the restoration of function in people with nervous system injury [9] or stroke [10].”
From A new high-density (25 electrodes/mm2) penetrating microelectrode array for recording and stimulating sub-millimeter neuroanatomical structures (2013) · cites this paper as [10]
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“Recent penetrating neural interface applications are requiring a greater number of electrodes (>60) to achieve complex neuromodulation [9,10,16] or neurophysiological [8] goals.”
From A new high-density (25 electrodes/mm2) penetrating microelectrode array for recording and stimulating sub-millimeter neuroanatomical structures (2013) · cites this paper as [10]
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Cites
Neuronal ensemble control of prosthetic devices by a human with tetraplegia
Hochberg, L. R., Serruya, M. D., Friehs, G. M., Mukand, J. A., Saleh, M., Caplan, A. H., Branner, A., Chen, D., Penn, R. D., & Donoghue, J. P. (2006) · Nature · doi:10.1038/nature04970 · Cited by 3,295
Self-citationCortical control of a prosthetic arm for self-feeding
Velliste, M., Perel, S., Spalding, M. C., Whitford, A. S., & Schwartz, A. B. (2008) · Nature · doi:10.1038/nature06996 · Cited by 1,591
Direct Cortical Control of 3D Neuroprosthetic Devices
Taylor, D. M., Tillery, S. I. H., & Schwartz, A. B. (2002) · Science · doi:10.1126/science.1070291 · Cited by 1,542
Brain-Controlled Interfaces: Movement Restoration with Neural Prosthetics
Schwartz, A. B., Cui, X. T., Weber, D. J., & Moran, D. W. (2006) · Neuron · doi:10.1016/j.neuron.2006.09.019 · Cited by 792
A high-performance brain–computer interface
Santhanam, G., Ryu, S. I., Yu, B. M., Afshar, A., & Shenoy, K. V. (2006) · Nature · doi:10.1038/nature04968 · Cited by 698
Cognitive Control Signals for Neural Prosthetics
Musallam, S., Corneil, B. D., Greger, B., Scherberger, H., & Andersen, R. A. (2004) · Science · doi:10.1126/science.1097938 · Cited by 663
The DLR lightweight robot: design and control concepts for robots in human environments
Albu‐Schäffer, A., Haddadin, S., Ott, C., Stemmer, A., Wimböck, T., & Hirzinger, G. (2007) · Industrial Robot the international journal of robotics research and application · doi:10.1108/01439910710774386 · Cited by 624
Self-citationDirect 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
Albu‐Schäffer, A., Eiberger, O., Grebenstein, M., Haddadin, S., Ott, C., Wimböck, T., Wolf, S., & Hirzinger, G. (2008) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2008.927979 · Cited by 509
Self-citationSimeral, J. D., Kim, S.-P., Black, M. J., Donoghue, J. P., & Hochberg, L. R. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/2/025027 · Cited by 496
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- Burrow, M., Dugger, J., Humphrey, D. R., Reed, D. J. & Hochberg, L. R. in Proc. ICORR ’97: Int. Conf. Rehabilitation Robotics 83–86 (Bath Institute of Medical Engineering, 1997)
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Sorted by citations
Items 601–700 of 1,013
Towards Rehabilitation Robotics: Off-the-Shelf BCI Control of Anthropomorphic Robotic Arms
Athanasiou, A., Xygonakis, I., Pandria, N., Kartsidis, P. E., Arfaras, G., Kavazidi, K. R., Foroglou, N., Astaras, A., & Bamidis, P. D. (2017) · BioMed Research International · doi:10.1155/2017/5708937 · Cited by 44
The Impact of Different Visual Feedbacks in User Training on Motor Imagery Control in BCI
Zapała, D., Francuz, P., Zapała, E., Kopiś-Posiej, N., Wierzgała, P., Augustynowicz, P., Majkowski, A., & Kołodziej, M. (2017) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-017-9383-z · Cited by 46
Ereifej, E. S., Smith, C. S., Meade, S. M., Chen, K., Feng, H., & Capadona, J. R. (2017) · Advanced Functional Materials · doi:10.1002/adfm.201704420 · Cited by 45
Koch, J., Schüettler, M., Pasluosta, C. F., & Stieglitz, T. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab36df · Cited by 44
Is Graphene Shortening the Path toward Spinal Cord Regeneration?
Girão, A. F., Serrano, M. C., Completo, A., & Marques, P. A. A. P. (2022) · ACS Nano · doi:10.1021/acsnano.2c04756 · Cited by 44
Tacca, N. J., Dunlap, C. F., Donegan, S., Hardin, J. O., Meyers, E., Darrow, M., Colachis, S. C., Gillman, A. S., & Friedenberg, D. A. (2024) · Scientific Reports · doi:10.1038/s41598-024-64458-x · Cited by 48
Decoding bipedal locomotion from the rat sensorimotor cortex
Rigosa, J., Panarese, A., Dominici, N., Friedli, L., van den Brand, R., Carpaneto, J., DiGiovanna, J., Courtine, G., & Micera, S. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/5/056014 · Cited by 44
Zamani, M., & Demosthenous, A. (2014) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2014.2309678 · Cited by 43
Non-causal spike filtering improves decoding of movement intention for intracortical BCIs
Masse, N. Y., Jarosiewicz, B., Simeral, J. D., Bacher, D., Stavisky, S. D., Cash, S. S., Oakley, E. M., Berhanu, E., Eskandar, E. N., Friehs, G. M., et al. (2014) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2014.08.004 · Cited by 43
Self-citationControlling an avatar by thought using real-time fMRI
Cohen, O., Koppel, M., Malach, R., & Friedman, D. A. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/3/035006 · Cited by 43
Workshops of the Fifth International Brain-Computer Interface Meeting: Defining the Future
Huggins, J. E., Guger, C., Allison, B. Z., Anderson, C. W., Batista, A. P., Brouwer, A.-M., Brunner, C., Chavarriaga, R., Fried-Oken, M. B., Gunduz, A., et al. (2014) · Brain-Computer Interfaces · doi:10.1080/2326263x.2013.876724 · Cited by 43
A closed-loop neurobotic system for fine touch sensing
Bologna, L. L., Pinoteau, J., Passot, J.-B., Garrido, J. A., Vogel, J., Vidal, E. R., & Arleo, A. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/4/046019 · Cited by 44
Self-citationAn ovine model of cerebral catheter venography for implantation of an endovascular neural interface
Oxley, T. J., Opie, N. L., Rind, G. S., Liyanage, K., John, S. E., Ronayne, S. M., McDonald, A. J., Dornom, A., Lovell, T. J. H., Mitchell, P. J., et al. (2017) · Journal of neurosurgery · doi:10.3171/2016.11.jns161754 · Cited by 43
Random Cropping Ensemble Neural Network for Image Classification in a Robotic Arm Grasping System
Cheng, B. H., Wu, W., Tao, D., Mei, S., Mao, T., & Cheng, J. S. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.2976420 · Cited by 43
Decoding hand kinematics from population responses in sensorimotor cortex during grasping
Okorokova, E. V., Goodman, J. M., Hatsopoulos, N. G., & Bensmaı̈a, S. J. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab95ea · Cited by 43
Teleoperation with Intelligent and Customizable Interfaces
Dragan, A. D., Srinivasa, S. S., & Lee, K. K. (2013) · Journal of Human-Robot Interaction · doi:10.5898/jhri.2.2.dragan · Cited by 43
High-density mapping of primate digit representations with a 1152-channel µECoG array
Kaiju, T., Inoue, M., Hirata, M., & Suzuki, T. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abe245 · Cited by 42
Schicktanz, S., Amelung, T., & Rieger, J. W. (2015) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2015.00064 · Cited by 42
Eliseyev, A., Auboiroux, V., Costecalde, T., Langar, L., Charvet, G., Mestais, C., Aksenova, T., & Benabid, A.-L. (2017) · Scientific Reports · doi:10.1038/s41598-017-16579-9 · Cited by 42
Yang, Q., Wu, B., Eles, J. R., Vazquez, A. L., Kozai, T. D. Y., & Cui, X. T. (2020) · Advanced Biosystems · doi:10.1002/adbi.201900287 · Cited by 43
Brain–Machine Interfaces: Closed-Loop Control in an Adaptive System
Sorrell, E., Rule, M. E., & O’Leary, T. (2021) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-061720-012348 · Cited by 41
Neurotechnologies to restore hand functions
Losanno, E., Mender, M. J., Chestek, C. A., Shokur, S., & Micera, S. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00054-4 · Cited by 45
Advanced rehabilitation in ischaemic stroke research
Wang, J., Li, Y., Qi, L., Mamtilahun, M., Liu, C., Liu, Z., Shi, R., Wu, S., & Yang, G. (2023) · Stroke and Vascular Neurology · doi:10.1136/svn-2022-002285 · Cited by 44
EEG-Based Motor BCIs for Upper Limb Movement: Current Techniques and Future Insights
Wang, J., Bi, L., & Fei, W. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3330500 · Cited by 44
Herring, E. Z., Graczyk, E. L., Memberg, W. D., Adams, R., Baca-Vaca, G. F., Hutchison, B. C., Krall, J. T., Alexander, B. J., Conlan, E. C., Alfaro, K. E., et al. (2023) · Neurosurgery · doi:10.1227/neu.0000000000002769 · Cited by 44
Vargas-Irwin, C. E., Franquemont, L., Black, M. J., & Donoghue, J. P. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.1574-15.2015 · Cited by 44
Self-citationComparing temporal aspects of visual, tactile, and microstimulation feedback for motor control
Godlove, J. M., Whaite, E. O., & Batista, A. P. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/4/046025 · Cited by 46
Towards autonomous neuroprosthetic control using Hebbian reinforcement learning
Mahmoudi, B., Pohlmeyer, E. A., Prins, N. W., Geng, S., & Sanchez, J. C. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/6/066005 · Cited by 42
Kim, Y. J., Park, S. W., Yeom, H. G., Bang, M. S., Kim, J. S., Chung, C. K., & Kim, S. (2015) · BioMedical Engineering OnLine · doi:10.1186/s12938-015-0075-8 · Cited by 42
Exercise: Why It is a Challenge for Both the Nonconscious and Conscious Mind
Iso‐Ahola, S. E. (2012) · Review of General Psychology · doi:10.1037/a0030657 · Cited by 48
Vargas-Irwin, C. E., Feldman, J. M., King, B., Simeral, J. D., Sorice, B. L., Oakley, E. M., Cash, S. S., Eskandar, E. N., Friehs, G. M., Hochberg, L. R., et al. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00450 · Cited by 45
Self-citationSakellaridi, S., Christopoulos, V. N., Aflalo, T., Pejsa, K., Rosario, E. R., Ouellette, D. S., Pouratian, N., & Andersen, R. A. (2019) · Neuron · doi:10.1016/j.neuron.2019.02.012 · Cited by 43
Toward the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes
Yu, Z., Liu, J. K., Jia, S., Zhang, Y., Zheng, Y., Tian, Y., & Huang, T. (2020) · Engineering · doi:10.1016/j.eng.2020.02.004 · Cited by 43
Self-citationPrediction of Imagined Single-Joint Movements in a Person With High-Level Tetraplegia
Ajiboye, A. B., Simeral, J. D., Donoghue, J. P., Hochberg, L. R., & Kirsch, R. F. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2209882 · Cited by 44
Self-citationDigital implementation of Hodgkin–Huxley neuron model for neurological diseases studies
Levi, T., Khoyratee, F., Saïghi, S., & Ikeuchi, Y. (2017) · Artificial Life and Robotics · doi:10.1007/s10015-017-0397-7 · Cited by 44
Superhuman Enhancements via Implants: Beyond the Human Mind
Warwick, K. (2020) · Philosophies · doi:10.3390/philosophies5030014 · Cited by 43
Multi-scale neural decoding and analysis
Lu, H., Lorenc, E. S., Zhu, H., Kilmarx, J., Sulzer, J. S., Xie, C., Tobler, P. N., Watrous, A. J., Orsborn, A. L., Lewis‐Peacock, J. A., et al. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac160f · Cited by 47
CSP-Net: Common spatial pattern empowered neural networks for EEG-based motor imagery classification
Jiang, X., Meng, L., Chen, X., Xu, Y., & Wu, D. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112668 · Cited by 45
Natraj, N., Seko, S., Abiri, R., Miao, R., Yan, H., Graham, Y., Tu-Chan, A., Chang, E. F., & Ganguly, K. (2025) · Cell · doi:10.1016/j.cell.2025.02.001 · Cited by 45
Prediction of Hand Trajectory from Electrocorticography Signals in Primary Motor Cortex
Chen, C., Shin, D., Watanabe, H., Nakanishi, Y., Kambara, H., Yoshimura, N., Nambu, A., Isa, T., Nishimura, Y., & Koike, Y. (2013) · PLoS ONE · doi:10.1371/journal.pone.0083534 · Cited by 41
Augmentative and Alternative Communication for People with Progressive Neuromuscular Disease
Ball, L. J., Fager, S. K., & Fried-Oken, M. B. (2012) · Physical Medicine and Rehabilitation Clinics of North America · doi:10.1016/j.pmr.2012.06.003 · Cited by 41
Enabling Low-Power, Multi-Modal Neural Interfaces Through a Common, Low-Bandwidth Feature Space
Irwin, Z. T., Thompson, D. E., Schroeder, K. E., Tat, D. M., Hassani, A., Bullard, A. J., Woo, S. L., Urbanchek, M. G., Sachs, A. J., Cederna, P. S., et al. (2015) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2501752 · Cited by 43
Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces
Kao, J. C., Ryu, S. I., & Shenoy, K. V. (2017) · Scientific Reports · doi:10.1038/s41598-017-06029-x · Cited by 42
An Intention-Driven Semi-autonomous Intelligent Robotic System for Drinking
Zhang, Z., Huang, Y., Chen, S., Qu, J., Pan, X., Yu, T., & Li, Y. (2017) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2017.00048 · Cited by 41
Progress in Neuroengineering for brain repair: New challenges and open issues
Panuccio, G., Semprini, M., Natale, L., Buccelli, S., Colombi, I., & Chiappalone, M. (2018) · Brain and Neuroscience Advances · doi:10.1177/2398212818776475 · Cited by 44
Wellman, S. M., Cambi, F., & Kozai, T. D. Y. (2018) · Biomaterials · doi:10.1016/j.biomaterials.2018.08.046 · Cited by 43
Exploring representations of human grasping in neural, muscle and kinematic signals
Sburlea, A. I., & Müller-Putz, G. (2018) · Scientific Reports · doi:10.1038/s41598-018-35018-x · Cited by 43
Joshi‐Imre, A., Black, B., Abbott, J., Kanneganti, A., Rihani, R. T., Chakraborty, B., Danda, V. R., Maeng, J., Sharma, R., Rieth, L. W., et al. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab1bc8 · Cited by 44
Monolayer Graphene Coating of Intracortical Probes for Long‐Lasting Neural Activity Monitoring
Bourrier, A., Shkorbatova, P. Y., Bonizzato, M., Rey, E., Barraud, Q., Courtine, G., Othmen, R., Reita, V., Bouchiat, V., & Delacour, C. (2019) · Advanced Healthcare Materials · doi:10.1002/adhm.201801331 · Cited by 43
Kazim, S. F., Bowers, C. A., Cole, C. D., Varela, S., Karimov, Z., Martinez, E., Ogulnick, J. V., & Schmidt, M. H. (2021) · Molecular Neurobiology · doi:10.1007/s12035-021-02484-w · Cited by 45
The marmoset as a model system for studying voluntary motor control
Walker, J. D., MacLean, J. N., & Hatsopoulos, N. G. (2016) · Developmental Neurobiology · doi:10.1002/dneu.22461 · Cited by 45
Skomrock, N. D., Schwemmer, M. A., Ting, J. E., Trivedi, H. R., Sharma, G., Bockbrader, M. A., & Friedenberg, D. A. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00763 · Cited by 44
Li, J., Li, Z., Chen, F., Bicchi, A., Sun, Y., & Fukuda, T. (2019) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2019.2897618 · Cited by 41
When I am (almost) 64: The effect of normal ageing on implicit motor imagery in young elderlies
Zapparoli, L., Saetta, G., Santis, C. D., Gandola, M., Zerbi, A., Banfi, G., & Paulesu, E. (2016) · Behavioural Brain Research · doi:10.1016/j.bbr.2016.01.058 · Cited by 41
Engineering and commercialization of human-device interfaces, from bone to brain
Tate, M. L. K., Detamore, M. S., Capadona, J. R., Woolley, A. J., & Knothe, U. R. (2016) · Biomaterials · doi:10.1016/j.biomaterials.2016.03.038 · Cited by 40
Cortical and subcortical mechanisms of brain‐machine interfaces
Marchesotti, S., Martuzzi, R., Schurger, A., Blefari, M. L., Millán, J. D. R., Bleuler, H., & Blanke, O. (2017) · Human Brain Mapping · doi:10.1002/hbm.23566 · Cited by 42
Lee, H. C., Gaire, J., Currlin, S., McDermott, M. D., Park, K., & Otto, K. J. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00513 · Cited by 40
BCI-Controlled Assistive Manipulator: Developed Architecture and Experimental Results
Lillo, P. D., Arrichiello, F., Vito, D. D., & Antonelli, G. (2020) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.2979375 · Cited by 40
The Representation of Finger Movement and Force in Human Motor and Premotor Cortices
Flint, R. D., Tate, M. C., Li, K., Templer, J. W., Rosenow, J. M., Pandarinath, C., & Slutzky, M. W. (2020) · eNeuro · doi:10.1523/eneuro.0063-20.2020 · Cited by 40
Single-Unit Recordings Reveal the Selectivity of a Human Face Area
Decramer, T., Premereur, E., Zhu, Q., Paesschen, W. V., van Loon, J., Vanduffel, W., Taubert, J., Janssen, P., & Theys, T. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0349-21.2021 · Cited by 43
The Convergence of Machine and Biological Intelligence
Wu, Z., Reddy, R., Pan, G., Zheng, N., Verschure, P. F. M. J., Zhang, Q., Zheng, X., Príncipe, J. C., Kreilinger, A., Rohm, M., et al. (2013) · IEEE Intelligent Systems · doi:10.1109/mis.2013.137 · Cited by 40
Byun, D., Cho, S. J., & Kim, S. (2013) · Journal of Micromechanics and Microengineering · doi:10.1088/0960-1317/23/12/125010 · Cited by 39
Chronic stability of a neuroprosthesis comprising multiple adjacent Utah arrays in monkeys
Chen, X., Wang, F., Kooijmans, R. N., Klink, P. C., Boehler, C., Asplund, M., & Roelfsema, P. R. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/ace07e · Cited by 45
Attacking Human Implants: A New Generation of Cybercrime
Gasson, M. N., & Koops, B. (2013) · Law Innovation and Technology · doi:10.5235/17579961.5.2.248 · Cited by 42
The Chemistry of Cyborgs—Interfacing Technical Devices with Organisms
Giselbrecht, S., Rapp, B. E., & Niemeyer, C. M. (2013) · Angewandte Chemie International Edition · doi:10.1002/anie.201307495 · Cited by 39
Brain-Computer Interfacing and Virtual Reality
Friedman, D. A. (2015) · Springer eBooks · doi:10.1007/978-981-4560-52-8_2-1 · Cited by 39
Decoding Grasping Movements from the Parieto-Frontal Reaching Circuit in the Nonhuman Primate
Nelissen, K., Fiave, P. A., & Vanduffel, W. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx037 · Cited by 43
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Bouton, C. E. (2017) · Journal of Internal Medicine · doi:10.1111/joim.12610 · Cited by 40
Veliev, F., Han, Z., Kalita, D., Briançon‐Marjollet, A., Bouchiat, V., & Delacour, C. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00466 · Cited by 42
Object discrimination using electrotactile feedback
Arakeri, T. J., Hasse, B. A., & Fuglevand, A. J. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aabc9a · Cited by 40
An EOG-based wheelchair robotic arm system for assisting patients with severe spinal cord injuries
Huang, Q., Yang, C., Zhang, Z., He, S., Zhang, R., Liu, J., Zhang, Y., Shao, M., & Li, Y. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/aafc88 · Cited by 39
Self-citationAdvanced technologies for intuitive control and sensation of prosthetics
Wolf, E. J., Cruz, T. H., Emondi, A. A., Langhals, N. B., Naufel, S. N., Peng, G. C. Y., Schulz, B. W., & Wolfson, M. B. (2019) · Biomedical Engineering Letters · doi:10.1007/s13534-019-00127-7 · Cited by 39
Tinkhauser, G., & Moraud, E. M. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.734186 · Cited by 39
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Kadan-Jamal, K., Wronowski, F., Jin, A. T., Naegele, T. E., Montes, V. R., Tao, X., Dominguez‐Alfaro, A., Lee, C., & Malliaras, G. G. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00468 · Cited by 39
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Ambroise, M., Levi, T., Bornat, Y., & Saïghi, S. (2013) · Conference on Information Sciences and Systems · doi:10.1109/ciss.2013.6616689 · Cited by 40
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Éthier, C., & Miller, L. E. (2014) · Neurobiology of Disease · doi:10.1016/j.nbd.2014.10.014 · Cited by 40
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Rupp, R., Kleih, S. C., Leeb, R., Millán, J. D. R., Kübler, A., & Müller-Putz, G. (2014) · The International library of ethics, law and technology · doi:10.1007/978-94-017-8996-7_2 · Cited by 42
Menz, V. K., Schaffelhofer, S., & Scherberger, H. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/5/056016 · Cited by 42
Khanna, P., Swann, N. C., de Hemptinne, C., Miocinovic, S., Miller, A. M., Starr, P. A., & Carmena, J. M. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2597243 · Cited by 38
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Cangelosi, A., & Invitto, S. (2017) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2016.2614423 · Cited by 38
Krucoff, M. O., Miller, J. P., Saxena, T. K., Bellamkonda, R., Rahimpour, S., Harward, S. C., Lad, S. P., & Turner, D. A. (2018) · Neurosurgery · doi:10.1093/neuros/nyy128 · Cited by 38
Annetta, N. V., Zhang, M., Mysiw, W. J., Rezai, A. R., Sharma, G., Friend, J., Schimmoeller, A., Buck, V. S., Friedenberg, D. A., Bouton, C. E., et al. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2864104 · Cited by 40
Thompson, C. H., Riggins, T. E., Patel, P. R., Chestek, C. A., Li, W., & Purcell, E. K. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab7030 · Cited by 38
Yamagiwa, S., Fujishiro, A., Sawahata, H., Numano, R., Ishida, M., & Kawano, T. (2014) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2014.09.048 · Cited by 39
Long-term stability of the chronic epidural wireless recorder WIMAGINE in tetraplegic patients
Larzabal, C., Bonnet, S., Costecalde, T., Auboiroux, V., Charvet, G., Chabardes, S., Aksenova, T., & Sauter-Starace, F. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac2003 · Cited by 37
Gao, W., Yan, Z., Zhou, H., Xie, Y., Wang, H., Yang, J., Yu, J., Ni, C., Liu, P., Xie, M., et al. (2025) · Journal of Nanobiotechnology · doi:10.1186/s12951-025-03573-x · Cited by 38
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Gharabaghi, A., Naros, G., Walter, A., Grimm, F., Schuermeyer, M., Roth, A., Bogdan, M., Rosenstiel, W., & Birbaumer, N. (2014) · Restorative Neurology and Neuroscience · doi:10.3233/rnn-140387 · Cited by 37
Hermann, J. K., Ravikumar, M., Shoffstall, A. J., Ereifej, E. S., Kovach, K. M., Chang, J. B., Soffer, A., Wong, C., Ali, A., Smith, P., et al. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aaa03e · Cited by 38
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Vitale, F., Shen, W., Driscoll, N., Burrell, J. C., Richardson, A. G., Adewole, D. O., Murphy, B. B., Ananthakrishnan, A., Oh, H., Wang, T., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0206137 · Cited by 39
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Park, Y. S., Cosgrove, G. R., Madsen, J. R., Eskandar, E. N., Hochberg, L. R., Cash, S. S., & Truccolo, W. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2921448 · Cited by 37
Self-citationLiu, C., Gao, J., Bi, Y., Shi, X., & Tian, D. (2020) · Sensors · doi:10.3390/s20123515 · Cited by 37
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Feng, Z., Sun, Y., Qian, L., Qi, Y., Wang, Y., Guan, C., & Sun, Y. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3115799 · Cited by 37
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Andersen, R. A., Aflalo, T., Bashford, L., Bjånes, D. A., & Kellis, S. (2022) · Annual Review of Psychology · doi:10.1146/annurev-psych-030221-030214 · Cited by 39
Semi-Implantable Bioelectronics
Fang, J., Huang, S., Liu, F., He, G., Li, X., Huang, X., Chen, H., & Xie, X. (2022) · Nano-Micro Letters · doi:10.1007/s40820-022-00818-4 · Cited by 37
Dynamic training protocol improves the robustness of PR-based myoelectric control
Yang, D., Gu, Y., Jiang, L., Osborn, L. E., & Liu, H. (2016) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2016.08.017 · Cited by 39
Quantum computing: the future of big data and artificial intelligence in spine
Mallow, G. M., Hornung, A., Barajas, J. N., Rudisill, S. S., An, H. S., & Samartzis, D. (2022) · Spine Surgery and Related Research · doi:10.22603/ssrr.2021-0251 · Cited by 36
Metadata
- Authors
- Hochberg, L. R., Bacher, D., Jarosiewicz, B., Masse, N. Y., Simeral, J. D., Vogel, J., Haddadin, S., Liu, J., Cash, S. S., van der Smagt, P., & Donoghue, J. P.
- Affiliations
- Brown University (US)
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
- Harvard University (US)
- Corresponding author
- Hochberg, Leigh R.
- Donoghue, John P.
- ORCID
- Venue
- Nature
- Published
- 2012-05
- Volume
- 485
- Issue
- 7398
- Pages
- 372-375
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3640850
- OpenAlex
- W2087704839
- Cited by
- 2,719
- References
- 41
- Citations by year
- 2026: 123 · 2025: 170 · 2024: 176 · 2023: 196 · 2022: 166 · 2021: 199 · 2020: 227 · 2019: 206 · 2018: 201 · 2017: 185 · 2016: 203 · 2015: 189 · 2014: 240 · 2013: 184 · 2012: 50
- FWCI
- 107.19
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- elib (German Aerospace Center) (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- mediaTUM (Technical University of Munich) (submittedVersion, other-oa)
- Funders
- U.S. Department of Veterans Affairs (A6779-I, B6453-R, B6310N)
- Doris Duke Charitable Foundation
- Craig H. Neilsen Foundation
- Office of Research and Development (A6779I, B6453R)
- European Commission (248587)
- National Institutes of Health (N01-HD-5-3403, n01hd53403-6-0-1, N01HD10018)
- Defense Advanced Research Projects Agency
- Rehabilitation Research and Development Service (B6453R, A6779I)
- Advanced Research Projects Agency
- National Institute on Deafness and Other Communication Disorders (R01DC009899)
- National Institute of Neurological Disorders and Stroke (NS-25074, R37NS025074, R01NS025074, R56 NS025074)
- National Institute of Biomedical Imaging and Bioengineering (R01EB007401)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (RC1HD063931, HHSN275201100018C, N01HD10018, N01 HD053403)
- National Center for Medical Rehabilitation Research (N01HD53403, N01HD10018)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Keywords
- tetraplegia, brain-computer interface, robot manipulators, neural interfaces, motor cortex, reach and grasp, upper limb paralysis, microelectrode arrays, long-term implantation, assistive devices, neural decoding, stroke, spinal cord injury, amyotrophic lateral sclerosis
- MeSH terms
- Aged, Arm, Calibration, Drinking, Female, Hand, Humans, Male, Man-Machine Systems, Microelectrodes, Middle Aged, Motor Cortex, Movement, Psychomotor Performance, Quadriplegia, Robotics, Time Factors, Hand Strength
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