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

Author

Listed:
  • Leigh R. Hochberg

    (Rehabilitation Research & Development Service
    School of Engineering and Institute for Brain Science, Brown University
    Massachusetts General Hospital
    Harvard Medical School)

  • Daniel Bacher

    (School of Engineering and Institute for Brain Science, Brown University)

  • Beata Jarosiewicz

    (Rehabilitation Research & Development Service
    Brown University)

  • Nicolas Y. Masse

    (Brown University)

  • John D. Simeral

    (Rehabilitation Research & Development Service
    School of Engineering and Institute for Brain Science, Brown University
    Massachusetts General Hospital)

  • Joern Vogel

    (German Aerospace Center, Institute of Robotics and Mechatronics (DLR)

  • Sami Haddadin

    (German Aerospace Center, Institute of Robotics and Mechatronics (DLR)

  • Jie Liu

    (Rehabilitation Research & Development Service
    School of Engineering and Institute for Brain Science, Brown University)

  • Sydney S. Cash

    (Massachusetts General Hospital
    Harvard Medical School)

  • Patrick van der Smagt

    (German Aerospace Center, Institute of Robotics and Mechatronics (DLR)

  • John P. Donoghue

    (Rehabilitation Research & Development Service
    School of Engineering and Institute for Brain Science, Brown University
    Brown University)

Abstract

Two people with long-standing tetraplegia use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements.

Suggested Citation

  • Leigh 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, 2012. "Reach and grasp by people with tetraplegia using a neurally controlled robotic arm," Nature, Nature, vol. 485(7398), pages 372-375, May.
    • Handle: RePEc:nat:nature:v:485:y:2012:i:7398:d:10.1038_nature11076
    DOI: 10.1038/nature11076
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    Cited by:

    1. Elisa Donati & Giacomo Valle, 2024. "Neuromorphic hardware for somatosensory neuroprostheses," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    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. Javier M Antelis & Luis Montesano & Ander Ramos-Murguialday & Niels Birbaumer & Javier Minguez, 2013. "On the Usage of Linear Regression Models to Reconstruct Limb Kinematics from Low Frequency EEG Signals," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-14, April.
    4. David Balderas & Pedro Ponce & Diego Lopez-Bernal & Arturo Molina, 2021. "Education 4.0: Teaching the Basis of Motor Imagery Classification Algorithms for Brain-Computer Interfaces," Future Internet, MDPI, vol. 13(8), pages 1-27, August.
    5. Xiangjing Wang & Chunsheng Chen & Li Zhu & Kailu Shi & Baocheng Peng & Yixin Zhu & Huiwu Mao & Haotian Long & Shuo Ke & Chuanyu Fu & Ying Zhu & Changjin Wan & Qing Wan, 2023. "Vertically integrated spiking cone photoreceptor arrays for color perception," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    6. 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.
    7. Baoguo Xu & Wenlong Li & Deping Liu & Kun Zhang & Minmin Miao & Guozheng Xu & Aiguo Song, 2022. "Continuous Hybrid BCI Control for Robotic Arm Using Noninvasive Electroencephalogram, Computer Vision, and Eye Tracking," Mathematics, MDPI, vol. 10(4), pages 1-20, February.
    8. Betz, Ulrich A.K. & Arora, Loukik & Assal, Reem A. & Azevedo, Hatylas & Baldwin, Jeremy & Becker, Michael S. & Bostock, Stefan & Cheng, Vinton & Egle, Tobias & Ferrari, Nicola & Schneider-Futschik, El, 2023. "Game changers in science and technology - now and beyond," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Fan Li & Jazlyn Gallego & Natasha N. Tirko & Jenna Greaser & Derek Bashe & Rudra Patel & Eric Shaker & Grace E. Valkenburg & Alanoud S. Alsubhi & Steven Wellman & Vanshika Singh & Camila Garcia Padill, 2024. "Low-intensity pulsed ultrasound stimulation (LIPUS) modulates microglial activation following intracortical microelectrode implantation," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    15. Fuji Ren & Yanwei Bao, 2020. "A Review on Human-Computer Interaction and Intelligent Robots," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 5-47, February.
    16. 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.
    17. Keundong Lee & Angelique C. Paulk & Yun Goo Ro & Daniel R. Cleary & Karen J. Tonsfeldt & Yoav Kfir & John S. Pezaris & Youngbin Tchoe & Jihwan Lee & Andrew M. Bourhis & Ritwik Vatsyayan & Joel R. Mart, 2024. "Flexible, scalable, high channel count stereo-electrode for recording in the human brain," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    18. Xiao-yu Sun & Bin Ye, 2023. "The functional differentiation of brain–computer interfaces (BCIs) and its ethical implications," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-9, December.
    19. Xingzhao Wang & Shun Wu & Hantao Yang & Yu Bao & Zhi Li & Changchun Gan & Yuanyuan Deng & Junyan Cao & Xue Li & Yun Wang & Chi Ren & Zhigang Yang & Zhengtuo Zhao, 2024. "Intravascular delivery of an ultraflexible neural electrode array for recordings of cortical spiking activity," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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