Jaimie M. Henderson

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.

Premotor cortex uses a compositional neural geometry to plan words

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

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.

Improved interpretability in LFADS models using a learned, context-dependent per-trial bias

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.

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.

Gesture encoding in human left precentral gyrus neuronal ensembles

This study analyzes neural activity in the precentral gyrus of two spinal cord injury patients to classify hand gestures with 70% accuracy, showing potential for intracortical BCI applications.

Error encoding in human speech motor cortex

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.

Pseudo-linear Summation explains Neural Geometry of Multi-finger Movements in Human Premotor Cortex

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

Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces

The paper measures neural signal instability in long-term intracortical recordings to improve brain-computer interface performance through recalibration timing.

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.

A mosaic of whole-body representations in human motor cortex

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

A flexible intracortical brain-computer interface for typing using finger movements

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

Translating deep learning to neuroprosthetic control

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.

Few-shot Algorithms for Consistent Neural Decoding (FALCON) Benchmark

The paper introduces FALCON, a benchmark suite for evaluating robustness of iBCI decoders using standardized datasets and evaluation metrics.

A high-performance speech neuroprosthesis

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.

Long-term unsupervised recalibration of cursor BCIs

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

High-performance brain-to-text communication via imagined handwriting

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.

Effects of Peripheral Haptic Feedback on Intracortical Brain-Computer Interface Control and Associated Sensory Responses in Motor Cortex

This study investigates how haptic feedback affects motor cortical control and iBCI performance in a tetraplegic participant.

The neural representation of force across grasp types in motor cortex of humans with tetraplegia

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.

Decoding spoken English phonemes from intracortical electrode arrays in dorsal precentral gyrus

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.

Hand Knob Area of Motor Cortex in People with Tetraplegia Represents the Whole Body in a Modular Way

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.

Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis

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.

Inferring single-trial neural population dynamics using sequential auto-encoders

This paper introduces LFADS, a deep learning method to infer single-trial neural population dynamics from spiking data, demonstrating improved accuracy in predicting behavior and extracting dynamics across datasets.