Payton H. Bechefsky

A generalizable speech neuroprosthesis

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.

Neural decoding of speech using deep neural ensembles

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

Encoding of speech modes and loudness in ventral precentral gyrus

This study decodes speech mode and loudness from ventral precentral gyrus activity in two participants, achieving high accuracy in predicting speech parameters and developing a closed-loop decoder for expressive speech neuroprostheses.

Cross-brain transfer of high-performance intracortical speech and handwriting BCIs

This paper demonstrates cross-brain transfer learning for intracortical BCIs decoding speech and handwriting, showing improved performance with limited training data and specific decoder design.

Preparatory encoding of diverse features of intended movement in the human motor cortex

This study investigates how preparatory activity in the human motor cortex encodes movement features like direction, curvature, and distance, and demonstrates its use for predictive brain-computer interface control.

Representation of Verbal Thought in Motor Cortex and Implications for Speech Neuroprostheses

The paper studies neural encoding of inner speech in motor cortex and shows it can be decoded, but also identifies a neural dimension that can prevent unintentional decoding of private thoughts.