Multi-User Speech BCI Model Needs Fewer Than 200 Sentences for a New User
UC Davis researchers trained a transformer-based speech decoder across six people with intracortical BCIs. The pooled model cut relative word error rates by more than 50% on average compared with subject-only models and, after fine-tuning on fewer than 200 sentences from an unseen user, achieved a word error rate below 7%. The bioRxiv preprint has not been peer reviewed.