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Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. ​

Literature Information ​

DOI10.1038/nature11076
PMID22596161
JournalNature
Impact Factor48.5
JCR QuartileQ1
Publication Year2012
Times Cited823
Keywordsneural interface system, tetraplegia, robotic arm, motor cortex, three-dimensional grasp
Literature TypeJournal Article, Research Support, N.I.H., Extramural, Research Support, U.S. Gov't, Non-P.H.S.
ISSN0028-0836
Pages372-5
Issue485(7398)
AuthorsLeigh R Hochberg, Daniel Bacher, Beata Jarosiewicz, Nicolas Y Masse, John D Simeral, Joern Vogel, Sami Haddadin, Jie Liu, Sydney S Cash, Patrick van der Smagt, John P Donoghue

TL;DR ​

This study investigates the potential of a neural interface system to enable individuals with long-standing tetraplegia to control a robotic arm for complex movements like reaching and grasping. The findings reveal that participants could perform these actions using signals from a small group of motor cortex neurons, highlighting the feasibility of restoring functional independence in individuals with severe paralysis even years after injury.

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neural interface system · tetraplegia · robotic arm · motor cortex · three-dimensional grasp

Abstract ​

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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Primary Questions Addressed ​

  1. What are the potential long-term effects of using a neurally controlled robotic arm on the quality of life for individuals with tetraplegia?
  2. How does the neural interface system adapt to different users with varying degrees of paralysis or neurological conditions?
  3. What are the limitations of current neural interface technologies in terms of precision and user control in robotic arm applications?
  4. In what ways could future advancements in neural interface systems improve the speed and accuracy of robotic movements for users with tetraplegia?
  5. How do the neural signals from individuals with tetraplegia compare to those of able-bodied individuals when controlling robotic devices?

Key Findings ​

Research Background and Objective ​

Paralysis resulting from spinal cord injury, brainstem stroke, or other neurological disorders significantly impairs voluntary movement. The aim of this study was to explore the feasibility of using a neural interface system (NIS) to enable individuals with longstanding tetraplegia to control a robotic arm for performing reach and grasp tasks, thereby restoring some level of independence and mobility.

Main Methods/Materials/Experimental Design ​

The study involved two participants with tetraplegia who used a 96-channel microelectrode array implanted in the motor cortex to decode neuronal activity. The experimental design included the following key steps:

  1. Neural Signal Acquisition:

    • Electrical potentials were recorded from the microelectrode array, filtered, and processed to extract action potentials (unit activity).
    • Signals were used to calibrate decoders that generated velocity and hand state commands.

  2. Control of Robotic Arms:

    • Participants controlled two robotic arms: the DLR Light-Weight Robot III and the DEKA Arm System.
    • The robotic arms were operated using decoded neuronal signals in a 3D space for reaching and grasping tasks.

  3. Task Execution:

    • Participants performed reach and grasp tasks targeting foam balls and a coffee bottle.
    • The task performance was assessed through visual inspections and recorded trials.

  4. Calibration and Decoding:

    • A Kalman filter was used for real-time decoding of intended hand velocity based on neuronal activity.
    • A linear discriminant classifier was employed to decode the intended hand state (open or closed).
Mermaid diagram

Key Results and Findings ​

  • Task Performance:
    • Participant S3 completed 158 trials across four sessions, achieving a touch success rate of 48.8% (DLR) and 69.2% (DEKA), with grasp success rates of 21.3% (DLR) and 46.2% (DEKA).
    • Participant T2 completed 45 trials with a touch success rate of 95.6% and a grasp success rate of 62.2%.
  • Daily Living Activity:
    • S3 successfully performed a drinking task using the robotic arm, marking the first time in over 14 years that she could drink independently.

Main Conclusion/Significance/Innovation ​

This study demonstrates that individuals with longstanding tetraplegia can effectively use a neural interface to control robotic arms for complex tasks such as reaching and grasping. The findings indicate that neuronal signals from a small population of motor cortex neurons can facilitate multidimensional control of robotic devices, offering a potential pathway to restore functional movement and independence in people with severe motor impairments.

Research Limitations and Future Directions ​

  • Limitations: The study's small sample size and the relatively slow speed and accuracy of the robotic arm movements compared to able-bodied individuals highlight the need for further improvements in the technology.
  • Future Directions: Further research is necessary to explore:
    • The long-term functionality of chronically implanted neural sensors.
    • The potential for more advanced decoders and additional training to enhance control capabilities.
    • The applicability of this technology in reanimating paralyzed muscles through functional electrical stimulation or in controlling prosthetic limbs for individuals with limb loss.

Overall, the study sets a foundation for future advancements in neural interface technology and its application in rehabilitation and assistive devices for individuals with paralysis.

References ​

  1. Bridging the brain to the world: a perspective on neural interface systems. - John P Donoghue - Neuron (2008)
  2. Challenges and opportunities for next-generation intracortically based neural prostheses. - Vikash Gilja;Cindy A Chestek;Ilka Diester;Jaimie M Henderson;Karl Deisseroth;Krishna V Shenoy - IEEE transactions on bio-medical engineering (2011)
  3. Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex. - Cynthia A Chestek;Vikash Gilja;Paul Nuyujukian;Justin D Foster;Joline M Fan;Matthew T Kaufman;Mark M Churchland;Zuley Rivera-Alvidrez;John P Cunningham;Stephen I Ryu;Krishna V Shenoy - Journal of neural engineering (2011)
  4. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. - Leigh R Hochberg;Mijail D Serruya;Gerhard M Friehs;Jon A Mukand;Maryam Saleh;Abraham H Caplan;Almut Branner;David Chen;Richard D Penn;John P Donoghue - Nature (2006)
  5. Control of a brain-computer interface without spike sorting. - George W Fraser;Steven M Chase;Andrew Whitford;Andrew B Schwartz - Journal of neural engineering (2009)
  6. Inference of hand movements from local field potentials in monkey motor cortex. - Carsten Mehring;Jörn Rickert;Eilon Vaadia;Simone Cardosa de Oliveira;Ad Aertsen;Stefan Rotter - Nature neuroscience (2003)
  7. Relationships among low-frequency local field potentials, spiking activity, and three-dimensional reach and grasp kinematics in primary motor and ventral premotor cortices. - Arjun K Bansal;Carlos E Vargas-Irwin;Wilson Truccolo;John P Donoghue - Journal of neurophysiology (2011)
  8. Cortical control of a prosthetic arm for self-feeding. - Meel Velliste;Sagi Perel;M Chance Spalding;Andrew S Whitford;Andrew B Schwartz - Nature (2008)
  9. Reliability of signals from a chronically implanted, silicon-based electrode array in non-human primate primary motor cortex. - Selim Suner;Matthew R Fellows;Carlos Vargas-Irwin;Gordon Kenji Nakata;John P Donoghue - IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society (2005)
  10. Cognitive control signals for neural prosthetics. - S Musallam;B D Corneil;B Greger;H Scherberger;R A Andersen - Science (New York, N.Y.) (2004)

Literatures Citing This Work ​

  1. Neuroscience: Brain-controlled robot grabs attention. - Andrew Jackson - Nature (2012)
  2. Neural repair and rehabilitation: Neurally controlled robotic arm enables tetraplegic patient to drink coffee of her own volition. - Katie Kingwell - Nature reviews. Neurology (2012)
  3. Prediction of imagined single-joint movements in a person with high-level tetraplegia. - A Bolu Ajiboye;John D Simeral;John P Donoghue;Leigh R Hochberg;Robert F Kirsch - IEEE transactions on bio-medical engineering (2012)
  4. Facilitation and restoration of cognitive function in primate prefrontal cortex by a neuroprosthesis that utilizes minicolumn-specific neural firing. - Robert E Hampson;Greg A Gerhardt;Vasilis Marmarelis;Dong Song;Ioan Opris;Lucas Santos;Theodore W Berger;Sam A Deadwyler - Journal of neural engineering (2012)
  5. Cognitive signals for brain-machine interfaces in posterior parietal cortex include continuous 3D trajectory commands. - Markus Hauschild;Grant H Mulliken;Igor Fineman;Gerald E Loeb;Richard A Andersen - Proceedings of the National Academy of Sciences of the United States of America (2012)
  6. From the bench to the bedside: Brain-machine interfaces in spinal cord injury, the blood-brain barrier, and neurodegeneration, using the hippocampus to improve cognition, metabolism, and epilepsy, and understanding axonal death. - Rich Everson;Jason S Hauptman - Surgical neurology international (2012)
  7. Neural interfaces for the brain and spinal cord--restoring motor function. - Andrew Jackson;Jonas B Zimmermann - Nature reviews. Neurology (2012)
  8. The next frontier in composite tissue allotransplantation. - Xiaoping Ren;Michael C Laugel - CNS neuroscience & therapeutics (2013)
  9. A high-performance neural prosthesis enabled by control algorithm design. - Vikash Gilja;Paul Nuyujukian;Cindy A Chestek;John P Cunningham;Byron M Yu;Joline M Fan;Mark M Churchland;Matthew T Kaufman;Jonathan C Kao;Stephen I Ryu;Krishna V Shenoy - Nature neuroscience (2012)
  10. Intention concepts and brain-machine interfacing. - Franziska Thinnes-Elker;Olga Iljina;John Kyle Apostolides;Felicitas Kraemer;Andreas Schulze-Bonhage;Ad Aertsen;Tonio Ball - Frontiers in psychology (2012)

... (813 more literatures)

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