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High-performance neuroprosthetic control by an individual with tetraplegia
Open accessAbstract
Background Paralysis or amputation of an arm results in the loss of the ability to orient the hand and grasp, manipulate, and carry objects, functions that are essential for activities of daily living. Brain-machine interfaces could provide a solution to restoring many of these lost functions. We therefore tested whether an individual with tetraplegia could rapidly achieve neurological control of a high-performance prosthetic limb using this type of an interface.Methods We implanted two 96-channel intracortical microelectrodes in the motor cortex of a 52-year-old individual with tetraplegia. Brain-machine-interface training was done for 13 weeks with the goal of controlling an anthropomorphic prosthetic limb with seven degrees of freedom (three-dimensional translation, three-dimensional orientation, one-dimensional grasping). The participant's ability to control the prosthetic limb was assessed with clinical measures of upper limb function. This study is registered with ClinicalTrials.gov, NCT01364480.Findings The participant was able to move the prosthetic limb freely in the three-dimensional workspace on the second day of training. After 13 weeks, robust seven-dimensional movements were performed routinely. Mean success rate on target-based reaching tasks was 91·6% (SD 4·4) versus median chance level 6·2% (95% CI 2·0-15·3). Improvements were seen in completion time (decreased from a mean of 148 s [SD 60] to 112 s [6]) and path efficiency (increased from 0·30 [0·04] to 0·38 [0·02]). The participant was also able to use the prosthetic limb to do skilful and coordinated reach and grasp movements that resulted in clinically significant gains in tests of upper limb function. No adverse events were reported.Interpretation With continued development of neuroprosthetic limbs, individuals with long-term paralysis could recover the natural and intuitive command signals for hand placement, orientation, and reaching, allowing them to perform activities of daily living.Funding Defense Advanced Research Projects Agency, National Institutes of Health, Department of Veterans Affairs, and UPMC Rehabilitation Institute.
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“First, using the same fundamental neural decoding and control methods, two participants have achieved similar scores on functional tasks with vision alone (5, 6, 28).”
From A brain-computer interface that evokes tactile sensations improves robotic arm control (2021) · cites this paper as [6]
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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 Collinger et al., 2013
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“ICMS can also be employed to deliver localized tactile percepts with natural features as well as in human-brain-computer interface applications for better control using area S1 (Collinger et al., 2013; Wodlinger et al., 2015; Downey et al., 2016).”
From Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review. (2017) · cites this paper as Collinger et al., 2013
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“For depth electrodes, there is also the potential to implant multiple locations, widening coverage [21].”
From Ethical considerations for the use of brain–computer interfaces for cognitive enhancement (2024) · cites this paper as [21]
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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 [2]
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“and has enabled the highest BCI performance to date compared to other recording technologies (EEG, ECoG) for restoring communication7, arm control2,5, and general-purpose computer use39.”
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“By combining these innovations, it is now possible for a human patient to perform tasks with a bionic arm by thoughts alone (Collinger et al., 2013; Wodlinger et al., 2015).”
From Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review. (2017) · cites this paper as Collinger et al., 2013
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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 [26]
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Metadata
- Authors
- 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.
- Affiliations
- University of Pittsburgh (US)
- Corresponding author
- Schwartz, Andrew B
- ORCID
- Venue
- The Lancet
- Publisher
- Elsevier BV
- Published
- 2013-02
- Volume
- 381
- Issue
- 9866
- Pages
- 557-564
- ISSN
- 0099-5355 (ISSN-L)
- 0140-6736
- PMC
- PMC3641862
- OpenAlex
- W2144910141
- Cited by
- 1,848
- References
- 31
- Citations by year
- 2026: 87 · 2025: 120 · 2024: 120 · 2023: 140 · 2022: 123 · 2021: 143 · 2020: 157 · 2019: 143 · 2018: 147 · 2017: 129 · 2016: 145 · 2015: 144 · 2014: 158 · 2013: 90
- FWCI
- 64.30
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- The Lancet (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Defense
- University of Pittsburgh
- National Institutes of Health (UL1TR000005, 5r01ns050256-05, KL2TR000146)
- Defense Advanced Research Projects Agency
- National Institute of Neurological Disorders and Stroke (RC1NS070311, R01-NS050256)
- National Center for Advancing Translational Sciences
- 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
- brain-computer interface, tetraplegia, intracortical microelectrodes, motor cortex, upper limb function, reach-to-grasp, upper limb paralysis, activities of daily living, prosthetic control
- MeSH terms
- Arm, Artificial Limbs, Female, Humans, Microelectrodes, Middle Aged, Psychomotor Performance, Quadriplegia, Hand Strength, Brain-Computer Interfaces
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.
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