A high-performance speech neuroprosthesis | bioRxiv

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Speech brain-computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speaking movements into text1,2 or sound3,4.Early demonstrations, while promising, have not yet achieved accuracies high enough for communication of unconstrainted sentences from a large vocabulary1–5. Here, we demonstrate the first 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 due amyotrophic lateral sclerosis (ALS), achieved a 9.1% word error rate on a 50 word vocabulary (2.7 times fewer errors than the prior state of the art speech BCI2) and a 23.8% word error rate on a 125,000 word vocabulary (the first successful demonstration of large-vocabulary decoding). Our BCI decoded speech at 62 words per minute, which is 3.4 times faster than the prior record for any kind of BCI6 an

A high-performance speech neuroprosthesis | bioRxiv Skip to main content New Results A high-performance speech neuroprosthesis Francis Willett , Erin Kunz , Chaofei Fan , Donald Avansino , Guy Wilson , Eun Young Choi , Foram Kamdar , View ORCID Profile Leigh R. Hochberg , Shaul Druckmann , Krishna V. Shenoy , Jaimie M. Henderson doi: https://doi.org/10.1101/2023.01.21.524489 Francis Willett 1 Howard Hughes Medical Institute at Stanford University , Stanford, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: willett2{at

A high-performance speech neuroprosthesis | bioRxiv

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