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Cited by 1,848EEG and Brain-Computer InterfacesNeuroscience and Neural EngineeringStroke Rehabilitation and Recovery

High-performance neuroprosthetic control by an individual with tetraplegia

Open access
Collinger, Jennifer L, Wodlinger, Brian, Downey, John E, Wang, Wei, Tyler-Kabara, Elizabeth C, Weber, Douglas J, McMorland, Angus JC, Velliste, Meel +2 more·2013·The Lancet
DOI 10.1016/s0140-6736(12)61816-9PMC PMC3641862
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Abstract

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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  • Using the same fundamental neural decoding and control methods, two participants have achieved similar scores on functional tasks with vision alone.

    “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]

    The Methods (Observation-based calibration and neural decoding; Functional brain-control tasks) describe neural decoding and functional testing, and the Results (Clinical assessments) report an ARAT score of 15–17 for one participant. This paper does not establish that two participants achieved similar scores, and the retrieved passages do not explicitly say the tasks were performed with vision alone. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • The flexibility of ConvNets might be a limitation in some brain-signal decoding scenarios; if the user should learn to adapt brain signals to the decoding model, a simpler feature-based model might yield better results.

    “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

    The Methods describe a linear neural decoder, and the Results suggest the participant learned to modulate her brain activity to better fit its encoding model (page numbers not provided). The paper does not assess ConvNets or compare their performance with a simpler feature-based model, so it does not support the proposed performance advantage. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • ICMS can deliver localized tactile percepts with natural features and can be used in human-brain-computer interface applications for better control using area S1.

    “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

    The Discussion reports improved neuroprosthetic control by a person with tetraplegia, supporting the human brain–computer interface portion of the claim. But the Methods (Array implantation; page unavailable) place the electrodes in motor cortex, not S1, and the Discussion describes transmission of tactile feedback to sensory cortex as a future development. The paper does not report ICMS delivering localized, natural-feeling tactile percepts or improving control through S1. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • For depth electrodes, there is potential to implant multiple locations, widening coverage.

    “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]

    The Methods, under “Array implantation” (page unavailable), report that two intracortical microelectrode arrays were implanted in the left motor cortex. This supports the possibility of multiple implants, but the paper does not say they were placed in multiple locations to widen coverage or make that claim about depth electrodes generally. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Earlier intracortical studies found that after paralysis, motor cortex still encodes intended reaching, grasping, and cursor movements.

    “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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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
DOI
10.1016/s0140-6736(12)61816-9
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 · 2012: 1
FWCI
64.30
Type
journal article
Language
en
Open access
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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

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