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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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“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 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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“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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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
Self-citation
Research works printed but not linked: 1
- 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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Bai, X., Li, M., Qi, S., Ng, A. C. M., Ng, T., & Qian, W. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1133933 · Cited by 36
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Jung, T., Zeng, N., Fabbri, J. D., Eichler, G., Li, Z., Zabeh, E., Das, A., Willeke, K. F., Wingel, K., Dubey, A., et al. (2025) · Nature Electronics · doi:10.1038/s41928-025-01509-9 · Cited by 40
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Gharabaghi, A., Naros, G., Khademi, F., Jesser, J., Spüler, M., Walter, A., Bogdan, M., Rosenstiel, W., & Birbaumer, N. (2014) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2014.00429 · Cited by 38
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Corbett, E. A., Kording, K. P., & Perreault, E. J. (2013) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2013.2251664 · Cited by 36
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Hsieh, H.-L., Wong, Y. T., Pesaran, B., & Shanechi, M. M. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aaeb1a · Cited by 37
Li, T., Xue, T., Wang, B., & Zhang, J. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00381 · Cited by 36
Sosnik, R., & Zur, O. B. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab59a7 · Cited by 37
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Li, Z., Yang, C., & Burdet, E. (2016) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2016.2571786 · Cited by 36
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Guan, C., Aflalo, T., KADLEC, K. H., Gámez, J., Rosario, E. R., Bari, A., Pouratian, N., & Andersen, R. A. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/acd3b1 · Cited by 38
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Goering, S., Brown, A. I., & Klein, E. P. (2024) · The Hastings Center Report · doi:10.1002/hast.1566 · Cited by 35
Li, F., Gallego, J., Tirko, N. N., Greaser, J., Bashe, D., Patel, R., Shaker, E., Valkenburg, G. E. V., Alsubhi, A. S., Wellman, S. M., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49709-9 · Cited by 38
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Oh, S., Jekal, J., Won, J., Lim, K. S., Jeon, C., Park, J., Yeo, H.-G., Kim, Y. G., Lee, Y. H., Ha, L. J., et al. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01280-w · Cited by 35
Bjånes, D. A., Kellis, S., Nickl, R., Baker, B., Aflalo, T., Bashford, L., Chivukula, S., Fifer, M. S., Osborn, L. E., Christie, B., et al. (2025) · Acta Biomaterialia · doi:10.1016/j.actbio.2025.02.030 · Cited by 37
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Schaffelhofer, S., Sartori, M., Scherberger, H., & Farina, D. (2014) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2014.2364776 · Cited by 36
Oby, E. R., Perel, S., Sadtler, P. T., Ruff, D. A., Mischel, J., Montez, D. F., Cohen, M. R., Batista, A. P., & Chase, S. M. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/3/036009 · Cited by 35
Employing Whole-Body Control in Assistive Robotics
Iskandar, M., Quere, G., Hagengruber, A., Dietrich, A., & Vogel, J. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967772 · Cited by 35
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Jadavji, Z., Zhang, J. Y., Paffrath, B., Zewdie, E., & Kirton, A. (2021) · Stroke · doi:10.1161/strokeaha.120.030596 · Cited by 35
Chandrasekaran, S., Fifer, M. S., Bickel, S., Osborn, L. E., Herrero, J. L., Christie, B. P., Xu, J., Murphy, R. K. J., Singh, S., Glasser, M. F., et al. (2021) · Bioelectronic Medicine · doi:10.1186/s42234-021-00076-6 · Cited by 39
Duffau, H. (2021) · Revue neurologique (Paris) · doi:10.1016/j.neurol.2021.07.016 · Cited by 27
Recursive N-Way Partial Least Squares for Brain-Computer Interface
Eliseyev, A., & Aksenova, T. (2013) · PLoS ONE · doi:10.1371/journal.pone.0069962 · Cited by 35
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Tankus, A., Fried, I., & Shoham, S. (2013) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2013.05.005 · Cited by 35
Potter–Baker, K. A., & Capadona, J. R. (2015) · ACS Macro Letters · doi:10.1021/mz500743a · Cited by 35
Treľová, D., Salgarella, A. R., Ricotti, L., Giudetti, G., Cutrone, A., Šrámková, P., Zahoranová, A., Chorvat, D., Jr., Haško, D., Canale, C., et al. (2018) · Langmuir · doi:10.1021/acs.langmuir.8b00765 · Cited by 35
AJILE Movement Prediction: Multimodal Deep Learning for Natural Human Neural Recordings and Video
Wang, N., Farhadi, A., Rao, R. P. N., & Brunton, B. W. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11889 · Cited by 35
Choe, J. K., Kim, S., Lee, A., Choi, C., Cho, J., Jo, W., Song, M. H., Cha, C., & Kim, J. (2024) · Advanced Materials · doi:10.1002/adma.202311154 · Cited by 34
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Yang, D., Tian, G., Chen, J., Liu, Y., Fatima, E., Qiu, J., Malek, N. A. N. N., & Qi, D. (2025) · BMEMat · doi:10.1002/bmm2.12130 · Cited by 37
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Marathe, A. R., & Taylor, D. M. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/3/036015 · Cited by 34
Redondo‐Castro, E., Navarro, X., & García‐Alías, G. (2015) · Journal of Neurotrauma · doi:10.1089/neu.2015.4140 · Cited by 34
Augmenting intracortical brain-machine interface with neurally driven error detectors
Even-Chen, N., Stavisky, S. D., Kao, J. C., Ryu, S. I., & Shenoy, K. V. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa8dc1 · Cited by 35
Microfabricated intracortical extracellular matrix-microelectrodes for improving neural interfaces
Shen, W., Das, S., Vitale, F., Richardson, A. G., Ananthakrishnan, A., Struzyna, L. A., Brown, D. P., Song, N., Ramkumar, M., Lucas, T. H., et al. (2018) · Microsystems & Nanoengineering · doi:10.1038/s41378-018-0030-5 · Cited by 37
Demonstration of a portable intracortical brain-computer interface
Weiss, J. M., Gaunt, R. A., Franklin, R. K., Boninger, M. L., & Collinger, J. L. (2019) · Brain-Computer Interfaces · doi:10.1080/2326263x.2019.1709260 · Cited by 37
Soldozy, S., Young, S., Kumar, J. S., Capek, S., Felbaum, D. R., Jean, W. C., Park, M. S., & Syed, H. R. (2020) · Neurosurgical FOCUS · doi:10.3171/2020.4.focus20186 · Cited by 34
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Karageorgos, I., Sriram, K., Veselý, J., Wu, M., Powell, M. P., Borton, D. A., Manohar, R., & Bhattacharjee, A. (2020) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca45697.2020.00041 · Cited by 38
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Zhang, S., Wang, C., Linghu, C., Wang, S., & Song, J. (2020) · Journal of Applied Mechanics · doi:10.1115/1.4047858 · Cited by 34
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Harnessing Prefrontal Cognitive Signals for Brain–Machine Interfaces
Min, B., Chavarriaga, R., & Millán, J. D. R. (2017) · Trends in biotechnology · doi:10.1016/j.tibtech.2017.03.008 · Cited by 35
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Apollo, N. V., Jiang, J. L., Cheung, W., Baquier, S. H., Lai, A., Mirebedini, A., Foroughi, J., Wallace, G. G., Shivdasani, M. N., Prawer, S., et al. (2017) · Advanced Biosystems · doi:10.1002/adbi.201700187 · Cited by 34
Kalanithi, P. S. A., & Henderson, J. M. (2012) · International review of neurobiology · doi:10.1016/b978-0-12-404706-8.00010-3 · Cited by 34
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Yang, X., Castellini, C., Farina, D., & Liu, H. (2024) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2024.3358960 · Cited by 33
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Stavisky, S. D. (2025) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-110122-012818 · Cited by 42
Sensitivity to microstimulation of somatosensory cortex distributed over multiple electrodes
Kim, S., Callier, T., Tabot, G. A., Tenore, F. V., & Bensmaı̈a, S. J. (2015) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2015.00047 · Cited by 33
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Belić, J. J., & Faisal, A. A. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00027 · Cited by 33
Recent advances in bioelectric prostheses
Pasquina, P. F., Perry, B. N., Miller, M. E., Ling, G. S. F., & Tsao, J. W. (2015) · Neurology Clinical Practice · doi:10.1212/cpj.0000000000000132 · Cited by 33
Key considerations in designing a somatosensory neuroprosthesis
Delhaye, B. P., Saal, H. P., & Bensmaı̈a, S. J. (2016) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2016.11.001 · Cited by 35
Clinician awareness of brain computer interfaces: a Canadian national survey
Létourneau, S., Zewdie, E., Jadavji, Z., Andersen, J. C., Burkholder, L. M., & Kirton, A. (2020) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-019-0624-7 · Cited by 36
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Ma, C., Guo, W., Zhang, H., Samuel, O. W., Ji, X., Xu, L., & Li, G. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3097257 · Cited by 33
An, H., Nason, S. R., Lim, J., Kwon, K., Willsey, M. S., Patil, P. G., Kim, H.-S., Sylvester, D., Chestek, C. A., & Blaauw, D. T. (2022) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2022.3175926 · Cited by 33
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Bleichner, M. G., Jansma, J. M., Sellmeijer, J., Raemaekers, M. A. H., & Ramsey, N. F. (2013) · Brain Topography · doi:10.1007/s10548-013-0322-x · Cited by 33
Vadera, S., Marathe, A. R., Gonzalez-Martinez, J. A., & Taylor, D. M. (2013) · Neurosurgical FOCUS · doi:10.3171/2013.3.focus1373 · Cited by 33
Mandonnet, E., & Duffau, H. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00082 · Cited by 33
Lampe, T., Fiederer, L. D. J., Voelker, M., Knorr, A. G., Riedmiller, M., & Ball, T. (2014) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/2557500.2557533 · Cited by 34
Body ownership and a new proprioceptive role for muscle spindles
Butler, A. A., Héroux, M. E., & Gandevia, S. C. (2016) · Acta Physiologica · doi:10.1111/apha.12792 · Cited by 34
Truccolo, W. (2016) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2017.02.004 · Cited by 35
Frey, L., Bandaru, P., Zhang, Y. S., O'Kelly, K., Khademhosseini, A., & Shin, S. R. (2017) · Advanced Functional Materials · doi:10.1002/adfm.201702009 · Cited by 33
Dai, J., Zhang, P., Sun, H., Qiao, X., Zhao, Y., Ma, J., Li, S., Zhou, J., & Wang, C. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab0bfb · Cited by 33
BRAND: a platform for closed-loop experiments with deep network models
Ali, Y. H., Bodkin, K., Rigotti-Thompson, M., Patel, K., Card, N. S., Bhaduri, B., Nason-Tomaszewski, S. R., Mifsud, D. M., Hou, X., Nicolas, C., et al. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad3b3a · Cited by 32
Self-citationXue, Z., Zhang, Y., Li, H., Chen, H., Shen, S., & Du, H. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3417598 · Cited by 35
Transfer of information by BMI
Tehovnik, E. J., Woods, L., & Slocum, W. M. (2013) · Neuroscience · doi:10.1016/j.neuroscience.2013.10.003 · Cited by 32
Long-term decoding of movement force and direction with a wireless myoelectric implant
Morel, P., Ferrea, E., Taghizadeh, B., Audí, J. M. C., Ruff, R., Hoffmann, K., Lewis, S., Russold, M., Dietl, H., Saleh, L. A., et al. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/1/016002 · Cited by 32
Perruchoud, D., Pisotta, I., Carda, S., Murray, M. M., & Ionta, S. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/4/041001 · Cited by 32
Decoding Information for Grasping from the Macaque Dorsomedial Visual Stream
Filippini, M., Breveglieri, R., Akhras, M. A., Bosco, A., Chinellato, E., & Fattori, P. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.3077-16.2017 · Cited by 33
Zhang, M., Schwemmer, M. A., Ting, J. E., Majstorovic, C., Friedenberg, D. A., Bockbrader, M. A., Mysiw, W. J., Rezai, A. R., Annetta, N. V., Bouton, C. E., et al. (2018) · Bioelectronic Medicine · doi:10.1186/s42234-018-0011-x · Cited by 33
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Gaire, J., Lee, H. C., Hilborn, N., Ward, R., Regan, M., & Otto, K. J. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aae4b6 · Cited by 35
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Kato, K., Sawada, M., & Nishimura, Y. (2019) · Nature Communications · doi:10.1038/s41467-019-12647-y · Cited by 33
Carìa, A., da Rocha, J. L. D., Gallitto, G., Birbaumer, N., Sitaram, R., & Ramos‐Murguialday, A. (2019) · Neurotherapeutics · doi:10.1007/s13311-019-00816-2 · Cited by 32
The sensory representation of causally controlled objects
Clancy, K. B., & Mrsic‐Flogel, T. D. (2020) · Neuron · doi:10.1016/j.neuron.2020.12.001 · Cited by 32
Intracortical Microelectrode Array Unit Yield under Chronic Conditions: A Comparative Evaluation
Usoro, J. O., Sturgill, B. S., Musselman, K. C., Capadona, J. R., & Pancrazio, J. J. (2021) · Micromachines · doi:10.3390/mi12080972 · Cited by 34
Deep brain–machine interfaces: sensing and modulating the human deep brain
Sui, Y., Yu, H., Zhang, C., Chen, Y., Jiang, C., & Li, L. (2022) · National Science Review · doi:10.1093/nsr/nwac212 · Cited by 33
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Fukuma, R., Yanagisawa, T., Yorifuji, S., Kato, R., Yokoi, H., Hirata, M., Saitoh, Y., Kishima, H., Kamitani, Y., & Yoshimine, T. (2015) · PLoS ONE · doi:10.1371/journal.pone.0131547 · Cited by 32
Chen, C., Shin, D., Watanabe, H., Nakanishi, Y., Kambara, H., Yoshimura, N., Nambu, A., Isa, T., Nishimura, Y., & Koike, Y. (2014) · Neuroscience Research · doi:10.1016/j.neures.2014.03.010 · Cited by 33
Hammer, D. X., Lozzi, A., Abliz, E., Greenbaum, N., Agrawal, A., Krauthamer, V., & Welle, C. G. (2014) · Biomedical Optics Express · doi:10.1364/boe.5.002823 · Cited by 32
Lahr, J., Schwartz, C., Heimbach, B., Aertsen, A., Rickert, J., & Tonio Ball, A. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/4/043001 · Cited by 32
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Kim, J., Park, H., Bruce, J. A., MSPT, Rowles, D. M., Holbrook, J. S., Nardone, B., West, D. P., Laumann, A. E., UK, et al. (2014) · The Journal of Rehabilitation Research and Development · doi:10.1682/jrrd.2013.08.0178 · Cited by 32
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Vu, P. P., Chestek, C. A., Nason, S. R., Kung, T. A., Kemp, S. W. P., & Cederna, P. S. (2020) · Muscle & Nerve · doi:10.1002/mus.26860 · Cited by 33
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Hybrid EEG-NIRS based BCI for quadcopter control
Khan, M. J., Hong, K., Naseer, N., & Bhutta, M. R. (2015) · Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) · doi:10.1109/sice.2015.7285434 · Cited by 31
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Courtine, G., & Bloch, J. (2015) · Neuron · doi:10.1016/j.neuron.2015.02.039 · Cited by 31
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Bérces, Z., Tóth, K., Márton, G., Pál, I., Kováts-Megyesi, B., Fekete, Z., Ulbert, I., & Pongracz, A. (2016) · Scientific Reports · doi:10.1038/srep35944 · Cited by 31
Mahajan, R., & Bansal, D. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.09.158 · Cited by 32
Neurorestorative interventions involving bioelectronic implants after spinal cord injury
Cho, N., Squair, J. W., Bloch, J., & Courtine, G. (2019) · Bioelectronic Medicine · doi:10.1186/s42234-019-0027-x · Cited by 32
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Nataraj, R., Sanford, S., Shah, A., & Liu, M. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00126 · Cited by 33
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Vogel, J., Leidner, D. S., Hagengruber, A., Panzirsch, M., Bäuml, B., Denninger, M., Hillenbrand, U., Suchenwirth, L., Schmaus, P., Sewtz, M., et al. (2020) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2020.3032142 · Cited by 31
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Dekleva, B. M., Weiss, J. M., Boninger, M. L., & Collinger, J. L. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac16b2 · Cited by 33
Fu, J., Chen, S., & Jia, J. (2022) · Brain Sciences · doi:10.3390/brainsci13010056 · Cited by 31
Patient-Specific Cortical Electrodes for Sulcal and Gyral Implantation
Morris, S., Hirata, M., Sugata, H., Goto, T., Matsushita, K., Yanagisawa, T., Saitoh, Y., Kishima, H., & Yoshimine, T. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2329812 · Cited by 31
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Gowda, S., Orsborn, A. L., Overduin, S. A., Moorman, H. G., & Carmena, J. M. (2014) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2014.2309673 · Cited by 33
Eliseyev, A., & Aksenova, T. (2016) · PLoS ONE · doi:10.1371/journal.pone.0154878 · Cited by 32
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Agashe, H. A., Paek, A. Y., & Contreras-Vidal, J. L. (2016) · Progress in brain research · doi:10.1016/bs.pbr.2016.04.016 · Cited by 32
Triboelectric‐Based Transparent Secret Code
Yuan, Z., Du, X., Li, N., Yin, Y., Cao, R., Zhang, X., Zhao, S., Niu, H., Jiang, T., Xu, W., et al. (2018) · Advanced Science · doi:10.1002/advs.201700881 · Cited by 31
Bioinspired flexible electronics for seamless neural interfacing and chronic recording
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Goering, S., Brown, T., & Klein, E. P. (2021) · Philosophy Compass · doi:10.1111/phc3.12734 · Cited by 30
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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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