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A high-performance speech neuroprosthesis

Author

Listed:
  • Francis R. Willett

    (Howard Hughes Medical Institute at Stanford University)

  • Erin M. Kunz

    (Stanford University
    Stanford University)

  • Chaofei Fan

    (Stanford University)

  • Donald T. Avansino

    (Howard Hughes Medical Institute at Stanford University)

  • Guy H. Wilson

    (Stanford University)

  • Eun Young Choi

    (Stanford University)

  • Foram Kamdar

    (Stanford University)

  • Matthew F. Glasser

    (Washington University in St. Louis
    Washington University in St. Louis)

  • Leigh R. Hochberg

    (Providence VA Medical Center
    Brown University
    Harvard Medical School)

  • Shaul Druckmann

    (Stanford University)

  • Krishna V. Shenoy

    (Howard Hughes Medical Institute at Stanford University
    Stanford University
    Stanford University
    Stanford University)

  • Jaimie M. Henderson

    (Stanford University
    Stanford University)

Abstract

Speech brain–computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speech into text1,2 or sound3,4. Early demonstrations, although promising, have not yet achieved accuracies sufficiently high for communication of unconstrained sentences from a large vocabulary1–7. Here we demonstrate a speech-to-text BCI that records spiking activity from intracortical microelectrode arrays. Enabled by these high-resolution recordings, our study participant—who can no longer speak intelligibly owing to amyotrophic lateral sclerosis—achieved a 9.1% word error rate on a 50-word vocabulary (2.7 times fewer errors than the previous state-of-the-art speech BCI2) and a 23.8% word error rate on a 125,000-word vocabulary (the first successful demonstration, to our knowledge, of large-vocabulary decoding). Our participant’s attempted speech was decoded at 62 words per minute, which is 3.4 times as fast as the previous record8 and begins to approach the speed of natural conversation (160 words per minute9). Finally, we highlight two aspects of the neural code for speech that are encouraging for speech BCIs: spatially intermixed tuning to speech articulators that makes accurate decoding possible from only a small region of cortex, and a detailed articulatory representation of phonemes that persists years after paralysis. These results show a feasible path forward for restoring rapid communication to people with paralysis who can no longer speak.

Suggested Citation

  • Francis R. Willett & Erin M. Kunz & Chaofei Fan & Donald T. Avansino & Guy H. Wilson & Eun Young Choi & Foram Kamdar & Matthew F. Glasser & Leigh R. Hochberg & Shaul Druckmann & Krishna V. Shenoy & Ja, 2023. "A high-performance speech neuroprosthesis," Nature, Nature, vol. 620(7976), pages 1031-1036, August.
  • Handle: RePEc:nat:nature:v:620:y:2023:i:7976:d:10.1038_s41586-023-06377-x
    DOI: 10.1038/s41586-023-06377-x
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    Citations

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    Cited by:

    1. Robert Burgan, 2023. "Once More about Human Nature, Enhancement and Substitution [Ešte raz o ľudskej prirodzenosti, zdokonaľovaní a substitúcii]," E-LOGOS, Prague University of Economics and Business, vol. 2023(2), pages 4-55.
    2. repec:prg:jnlelg:v:preprint:id:499 is not listed on IDEAS
    3. Xiner Wang & Guo Bai & Jizhi Liang & Qianyang Xie & Zhaohan Chen & Erda Zhou & Meng Li & Xiaoling Wei & Liuyang Sun & Zhiyuan Zhang & Chi Yang & Tiger H. Tao & Zhitao Zhou, 2024. "Gustatory interface for operative assessment and taste decoding in patients with tongue cancer," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    4. Sarah K. Wandelt & David A. Bjånes & Kelsie Pejsa & Brian Lee & Charles Liu & Richard A. Andersen, 2024. "Representation of internal speech by single neurons in human supramarginal gyrus," Nature Human Behaviour, Nature, vol. 8(6), pages 1136-1149, June.

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