Abstract
Intracortical brain-computer interfaces (BCIs) can restore communication to people with vocal tract paralysis by decoding cortical activity during attempted speech into text. State-of-the-art systems pairing neural-to-phoneme decoders with phoneme-to-word language models have achieved word error rates (WERs) as low as 1%, but only after collecting thousands of sentences of training data. Shortening the data collection process would facilitate scaling this new technology by reducing the time from device implant to high-accuracy communication. Here we introduce a transformer-based decoder model trained jointly across six intracortical speech BCI participants. For every participant - regardless of sex, disease etiology, or attempted speaking strategy - a multi-user model decoded speech more accurately (over 50% lower relative WER on average) than models trained on individual users' data. Notably, the multi-user model could be fine-tuned on fewer than 200 sentences from a held-out user to achieve a WER below 7%. These results reveal how to pool intracortical data across people to yield more accurate, generalizable, and rapidly-deployable decoding models.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 28 Jul 2026.
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