Zachery Fogg

This paper introduces a multi-user transformer-based decoder for intracortical speech BCIs, achieving lower word error rates with less training data than individual models.

A paralyzed man with ALS used a long-term intracortical BCI for over two years to communicate, control a computer, and work independently.

This paper tests deep neural ensembles in real-time speech BCIs, showing reduced word error rates and introducing a computationally efficient pseudoensembling method.