The DOI matches its Crossref record; 39 of the 41 references it cites were found in a registry or an index.
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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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
Self-citation
Research works printed but not linked: 1
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Deep learning with convolutional neural networks for EEG decoding and visualization
Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W., & Ball, T. (2017) · Human Brain Mapping · doi:10.1002/hbm.23730 · Cited by 3,751
Hammock, M. L., Chortos, A., Tee, B. C. ‐., Tok, J. B. ‐., & Bao, Z. (2013) · Advanced Materials · doi:10.1002/adma.201302240 · Cited by 2,396
Enhancing Our Lives with Immersive Virtual Reality
Slater, M., & Sanchez-Vives, M. V. (2016) · Frontiers in Robotics and AI · doi:10.3389/frobt.2016.00074 · Cited by 1,993
High-performance neuroprosthetic control by an individual with tetraplegia
Collinger, J. L., Wodlinger, B., Downey, J. E., Wang, W., Tyler-Kabara, E. C., Weber, D. J., McMorland, A. J., Velliste, M., Boninger, M. L., & Schwartz, A. B. (2013) · The Lancet · doi:10.1016/s0140-6736(12)61816-9 · Cited by 1,848
An Integrated Brain-Machine Interface Platform With Thousands of Channels
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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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