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Cortical control of a prosthetic arm for self-feeding

Author

Listed:
  • Meel Velliste

    (School of Medicine, E1440 BST, Lothrop Street, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA)

  • Sagi Perel

    (749 Benedum Hall, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA
    Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA)

  • M. Chance Spalding

    (749 Benedum Hall, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA
    Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA)

  • Andrew S. Whitford

    (749 Benedum Hall, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA
    Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA)

  • Andrew B. Schwartz

    (School of Medicine, E1440 BST, Lothrop Street, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA
    749 Benedum Hall, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA
    Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
    University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA)

Abstract

Robotic limbs: Controlled by a thought Brain-machine interfaces have mostly been used previously to move cursors on computer displays. Now experiments on macaque monkeys show that brain activity signals can control a multi-jointed prosthetic device in real-time. The monkeys used motor cortical activity to control a human-like prosthetic arm in a self-feeding task, with a greater sophistication of control than previously possible. This work could be important for the development of more practical neuro-prosthetic devices in the future.

Suggested Citation

  • Meel Velliste & Sagi Perel & M. Chance Spalding & Andrew S. Whitford & Andrew B. Schwartz, 2008. "Cortical control of a prosthetic arm for self-feeding," Nature, Nature, vol. 453(7198), pages 1098-1101, June.
    • Handle: RePEc:nat:nature:v:453:y:2008:i:7198:d:10.1038_nature06996
    DOI: 10.1038/nature06996
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    Citations

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    Cited by:

    1. Tobias Pistohl & Thomas Sebastian Benedikt Schmidt & Tonio Ball & Andreas Schulze-Bonhage & Ad Aertsen & Carsten Mehring, 2013. "Grasp Detection from Human ECoG during Natural Reach-to-Grasp Movements," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-11, January.
    2. 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.
    3. Shinsuke Koyama & Uri Eden & Emery Brown & Robert Kass, 2010. "Bayesian decoding of neural spike trains," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 37-59, February.
    4. Andrey Eliseyev & Tetiana Aksenova, 2016. "Penalized Multi-Way Partial Least Squares for Smooth Trajectory Decoding from Electrocorticographic (ECoG) Recording," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-19, May.
    5. 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.
    6. 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.
    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. Jonathan A Michaels & Benjamin Dann & Hansjörg Scherberger, 2016. "Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-22, November.
    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. Han-Lin Hsieh & Maryam M Shanechi, 2018. "Optimizing the learning rate for adaptive estimation of neural encoding models," PLOS Computational Biology, Public Library of Science, vol. 14(5), pages 1-34, May.
    11. Sebastian Schleidgen & Orsolya Friedrich & Selin Gerlek & Galia Assadi & Johanna Seifert, 2023. "The concept of “interaction” in debates on human–machine interaction," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
    12. 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.
    13. Linlin Li & Shufang Zhao & Wenhao Ran & Zhexin Li & Yongxu Yan & Bowen Zhong & Zheng Lou & Lili Wang & Guozhen Shen, 2022. "Dual sensing signal decoupling based on tellurium anisotropy for VR interaction and neuro-reflex system application," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    14. 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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