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Real-time prediction of hand trajectory by ensembles of cortical neurons in primates

Author

Listed:
  • Johan Wessberg

    (Department of Neurobiology)

  • Christopher R. Stambaugh

    (Department of Neurobiology)

  • Jerald D. Kralik

    (Department of Neurobiology)

  • Pamela D. Beck

    (Department of Neurobiology)

  • Mark Laubach

    (Department of Neurobiology)

  • John K. Chapin

    (Department of Physiology and Pharmacology State University of New York Health Science Center)

  • Jung Kim

    (Laboratory for Human and Machine Haptics)

  • S. James Biggs

    (Laboratory for Human and Machine Haptics)

  • Mandayam A. Srinivasan

    (Laboratory for Human and Machine Haptics)

  • Miguel A. L. Nicolelis

    (Department of Neurobiology
    Department of Biomedical Engineering
    Duke University)

Abstract

Signals derived from the rat motor cortex can be used for controlling one-dimensional movements of a robot arm1. It remains unknown, however, whether real-time processing of cortical signals can be employed to reproduce, in a robotic device, the kind of complex arm movements used by primates to reach objects in space. Here we recorded the simultaneous activity of large populations of neurons, distributed in the premotor, primary motor and posterior parietal cortical areas, as non-human primates performed two distinct motor tasks. Accurate real-time predictions of one- and three-dimensional arm movement trajectories were obtained by applying both linear and nonlinear algorithms to cortical neuronal ensemble activity recorded from each animal. In addition, cortically derived signals were successfully used for real-time control of robotic devices, both locally and through the Internet. These results suggest that long-term control of complex prosthetic robot arm movements can be achieved by simple real-time transformations of neuronal population signals derived from multiple cortical areas in primates.

Suggested Citation

  • Johan Wessberg & Christopher R. Stambaugh & Jerald D. Kralik & Pamela D. Beck & Mark Laubach & John K. Chapin & Jung Kim & S. James Biggs & Mandayam A. Srinivasan & Miguel A. L. Nicolelis, 2000. "Real-time prediction of hand trajectory by ensembles of cortical neurons in primates," Nature, Nature, vol. 408(6810), pages 361-365, November.
  • Handle: RePEc:nat:nature:v:408:y:2000:i:6810:d:10.1038_35042582
    DOI: 10.1038/35042582
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    Cited by:

    1. Omer Hazon & Victor H. Minces & David P. Tomàs & Surya Ganguli & Mark J. Schnitzer & Pablo E. Jercog, 2022. "Noise correlations in neural ensemble activity limit the accuracy of hippocampal spatial representations," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    2. Pavlov, A.N. & Grishina, D.S. & Runnova, A.E. & Maksimenko, V.A. & Pavlova, O.N. & Shchukovsky, N.V. & Hramov, A.E. & Kurths, J., 2019. "Recognition of electroencephalographic patterns related to human movements or mental intentions with multiresolution analysis," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 230-235.
    3. 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.
    4. Alessandro Vato & Francois D Szymanski & Marianna Semprini & Ferdinando A Mussa-Ivaldi & Stefano Panzeri, 2014. "A Bidirectional Brain-Machine Interface Algorithm That Approximates Arbitrary Force-Fields," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-20, March.
    5. Hu Lu & Shengtao Yang & Longnian Lin & Baoming Li & Hui Wei, 2013. "Prediction of Rat Behavior Outcomes in Memory Tasks Using Functional Connections among Neurons," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-11, September.
    6. 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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