Erin M. Kunz

The paper shows that premotor cortex uses a compositional neural geometry to plan words by combining phoneme representations, enabling efficient planning of novel sequences.

This paper improves the interpretability of LFADS models by introducing a per-trial bias, enabling simpler contextual dynamics and better analysis of neural activity in simulated and real data.

The paper shows error detection signals in speech motor cortex during neuroprosthesis use, enabling error identification with 86% accuracy and revealing distinct patterns for different error types.

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.

This paper demonstrates a high-performance speech neuroprosthesis that achieves low word error rates and high decoding speeds using intracortical recordings from a person with ALS.