A generalizable speech neuroprosthesis
Zachery M. Fogg, Nicholas S. Card, Maitreyee Wairagkar, Aparna Srinivasan, Tyler Singer-Clark, Xianda Hou, Elizaveta Okorokova, Hamza Peracha, Carrina Iacobacci, Tiffany Brailow, Justin Jude, Hadar Levi-Aharoni, Trung Le, Domenick Mifsud, Pranav I. Deevi, Samuel R. Nason-Tomaszewski, Anna L. Pritchard, Yunnuo Zhang, Brice Richards, Payton Bechefsky, Leigh R. Hochberg, Ziv Williams, Kiarash Shahlaie, Nicholas AuYong, Daniel B. Rubin, Chethan Pandarinath, David M. Brandman, Sergey D. Stavisky
Published in bioRxiv (preprint), 2026
Summary
A transformer-based decoder trained jointly across six intracortical speech BCI participants outperforms single-user models for every participant, cutting relative word error rate by over 50% on average. A new user can reach under 7% word error rate after finetuning on fewer than 200 sentences.
Brain-Computer Interface, Machine Learning, Speech Neuroprosthesis bioRxiv