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Neuronal ensemble control of prosthetic devices by a human with tetraplegia

Author

Listed:
  • Leigh R. Hochberg

    (Brigham and Women's Hospital, and Spaulding Rehabilitation Hospital, Harvard Medical School
    Department of Neuroscience and Brain Science Program
    Veterans Health Administration)

  • Mijail D. Serruya

    (Department of Neuroscience and Brain Science Program
    Brown University)

  • Gerhard M. Friehs

    (Brown University
    Rhode Island Hospital)

  • Jon A. Mukand

    (Brown University
    Sargent Rehabilitation Center)

  • Maryam Saleh

    (Cyberkinetics Neurotechnology Systems, Inc.
    Graduate Program in Computational Neuroscience, University of Chicago)

  • Abraham H. Caplan

    (Cyberkinetics Neurotechnology Systems, Inc.)

  • Almut Branner

    (Cyberkinetics Neurotechnology Systems, Inc.)

  • David Chen

    (Rehabilitation Institute of Chicago)

  • Richard D. Penn

    (University of Chicago Hospitals)

  • John P. Donoghue

    (Department of Neuroscience and Brain Science Program
    Cyberkinetics Neurotechnology Systems, Inc.)

Abstract

Neuromotor prostheses (NMPs) aim to replace or restore lost motor functions in paralysed humans by routeing movement-related signals from the brain, around damaged parts of the nervous system, to external effectors. To translate preclinical results from intact animals to a clinically useful NMP, movement signals must persist in cortex after spinal cord injury and be engaged by movement intent when sensory inputs and limb movement are long absent. Furthermore, NMPs would require that intention-driven neuronal activity be converted into a control signal that enables useful tasks. Here we show initial results for a tetraplegic human (MN) using a pilot NMP. Neuronal ensemble activity recorded through a 96-microelectrode array implanted in primary motor cortex demonstrated that intended hand motion modulates cortical spiking patterns three years after spinal cord injury. Decoders were created, providing a ‘neural cursor’ with which MN opened simulated e-mail and operated devices such as a television, even while conversing. Furthermore, MN used neural control to open and close a prosthetic hand, and perform rudimentary actions with a multi-jointed robotic arm. These early results suggest that NMPs based upon intracortical neuronal ensemble spiking activity could provide a valuable new neurotechnology to restore independence for humans with paralysis.

Suggested Citation

  • 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, 2006. "Neuronal ensemble control of prosthetic devices by a human with tetraplegia," Nature, Nature, vol. 442(7099), pages 164-171, July.
    • Handle: RePEc:nat:nature:v:442:y:2006:i:7099:d:10.1038_nature04970
    DOI: 10.1038/nature04970
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    Cited by:

    1. No-Sang Kwak & Klaus-Robert Müller & Seong-Whan Lee, 2017. "A convolutional neural network for steady state visual evoked potential classification under ambulatory environment," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-20, February.
    2. Michael Riss, 2014. "FTSPlot: Fast Time Series Visualization for Large Datasets," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-16, April.
    3. Benjamin I Rapoport & Lorenzo Turicchia & Woradorn Wattanapanitch & Thomas J Davidson & Rahul Sarpeshkar, 2012. "Efficient Universal Computing Architectures for Decoding Neural Activity," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-13, September.
    4. Tomislav Milekovic & Tonio Ball & Andreas Schulze-Bonhage & Ad Aertsen & Carsten Mehring, 2013. "Detection of Error Related Neuronal Responses Recorded by Electrocorticography in Humans during Continuous Movements," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-20, February.
    5. Yasuhiko Nakanishi & Takufumi Yanagisawa & Duk Shin & Ryohei Fukuma & Chao Chen & Hiroyuki Kambara & Natsue Yoshimura & Masayuki Hirata & Toshiki Yoshimine & Yasuharu Koike, 2013. "Prediction of Three-Dimensional Arm Trajectories Based on ECoG Signals Recorded from Human Sensorimotor Cortex," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-9, August.
    6. Ke Yu & Hasan AI-Nashash & Nitish Thakor & Xiaoping Li, 2014. "The Analytic Bilinear Discrimination of Single-Trial EEG Signals in Rapid Image Triage," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-10, June.
    7. Andrés Úbeda & Enrique Hortal & Eduardo Iáñez & Carlos Perez-Vidal & Jose M Azorín, 2015. "Assessing Movement Factors in Upper Limb Kinematics Decoding from EEG Signals," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-12, May.
    8. Javier León & Juan José Escobar & Andrés Ortiz & Julio Ortega & Jesús González & Pedro Martín-Smith & John Q Gan & Miguel Damas, 2020. "Deep learning for EEG-based Motor Imagery classification: Accuracy-cost trade-off," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-30, June.
    9. Hong Gi Yeom & June Sic Kim & Chun Kee Chung, 2014. "High-Accuracy Brain-Machine Interfaces Using Feedback Information," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-7, July.
    10. Ujwal Chaudhary & Bin Xia & Stefano Silvoni & Leonardo G Cohen & Niels Birbaumer, 2017. "Brain–Computer Interface–Based Communication in the Completely Locked-In State," PLOS Biology, Public Library of Science, vol. 15(1), pages 1-25, January.
    11. Nuri F Ince & Rahul Gupta & Sami Arica & Ahmed H Tewfik & James Ashe & Giuseppe Pellizzer, 2010. "High Accuracy Decoding of Movement Target Direction in Non-Human Primates Based on Common Spatial Patterns of Local Field Potentials," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-11, December.
    12. Ujwal Chaudhary & Ioannis Vlachos & Jonas B. Zimmermann & Arnau Espinosa & Alessandro Tonin & Andres Jaramillo-Gonzalez & Majid Khalili-Ardali & Helge Topka & Jens Lehmberg & Gerhard M. Friehs & Alain, 2022. "Spelling interface using intracortical signals in a completely locked-in patient enabled via auditory neurofeedback training," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    13. Eric A Pohlmeyer & Babak Mahmoudi & Shijia Geng & Noeline W Prins & Justin C Sanchez, 2014. "Using Reinforcement Learning to Provide Stable Brain-Machine Interface Control Despite Neural Input Reorganization," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-12, January.
    14. Josh Merel & David Carlson & Liam Paninski & John P Cunningham, 2016. "Neuroprosthetic Decoder Training as Imitation Learning," PLOS Computational Biology, Public Library of Science, vol. 12(5), pages 1-24, May.
    15. Zheng Li & Joseph E O'Doherty & Timothy L Hanson & Mikhail A Lebedev & Craig S Henriquez & Miguel A L Nicolelis, 2009. "Unscented Kalman Filter for Brain-Machine Interfaces," PLOS ONE, Public Library of Science, vol. 4(7), pages 1-18, July.

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