Francis R. Willett

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

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

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 shows that neural activity in human premotor cortex combines finger movements through pseudo-linear summation, with deviations that improve decoding performance.

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.

The paper maps human motor cortex using microelectrode recordings, finding whole-body representations and challenging the traditional motor homunculus model.

This paper develops a flexible intracortical BCI for typing using finger movements, achieving high accuracy and speed in two typing paradigms.

This paper develops a high-performance brain-computer interface for finger control and quadcopter navigation, achieving fast target acquisition and obstacle course performance.

This paper shows that recurrent neural networks (RNNs) can be adapted for real-time neuroprosthetic control by modifying their training data's temporal structure, enabling better performance than standard linear methods.

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.

This paper introduces a hidden Markov model for unsupervised recalibration of intracortical brain-computer interfaces, showing improved performance over long-term use.

This paper demonstrates a brain-computer interface that decodes imagined handwriting movements into text at high speed and accuracy, closing the gap between BCI and able-bodied typing rates.

This study examines how force and grasp are represented in motor cortex of tetraplegic individuals, finding that grasp affects force decoding and that grasp is more accurately classified than force.

This study decodes 39 English phonemes from neural signals in the dorsal precentral gyrus using intracortical electrode arrays, achieving 33.9% classification accuracy with a recurrent neural network.

The paper investigates the representation of body movements in the motor cortex of tetraplegic individuals, finding a modular code that could improve brain-computer interfaces.

The paper records neural activity in the motor cortex of people with paralysis during speech and non-speaking movements, showing that neurons modulate during speech and revealing common neural dynamics between speech and arm movements.