Lee E. Miller

High-density surface EMG grid enables non-invasive characterization of intrinsic hand muscles activity

This paper introduces a high-density surface EMG grid for non-invasive recording of intrinsic hand muscle activity, showing it closely matches intramuscular EMG with improved spatial selectivity.

Muscle-driven hand simulations emphasize the critical role of the extensor mechanism

The paper uses biomechanical simulations to study hand motion, finding that extensor muscle path modifications improve accuracy in tracking interphalangeal joints during American Sign Language gestures.

A framework for quantifying the mechanics of dexterous grasp

The paper develops a framework to quantify the mechanics of primate dexterous grasp by measuring contact forces, kinematics, and joint torques using an experimental apparatus and musculoskeletal modeling.

Flexible integration of corollary discharge and sensory feedback signals in somatosensory cortex

The paper shows that motor and sensory signals in somatosensory cortex are integrated through orthogonal population subspaces to enable accurate body state estimation and perturbation decoding.

Overlap in neural representations of coordinated wrist and finger movements in human motor cortex

The paper investigates how motor cortex encodes wrist and finger movements, finding shared neural signals for flexion-extension across digits and wrist, and demonstrating improved BCI control by accounting for these constraints.

Consistent EMG encoding during reach and grasp by neurons in motor cortex

The paper shows that motor cortex neurons encode movement through a consistent muscle-based principle, explaining both reaching and grasping behaviors without distinct control strategies.

Evolution of object identity information in sensorimotor cortex throughout grasp

The paper examines how object identity is represented in sensorimotor cortex during the transition from hand opening to grasping, finding that neural representation shifts from motor and proprioceptive areas before contact to cutaneous and integrative somatosensory areas after contact.

Distinct neural modes carry information about grasp force and phase in the sensorimotor cortex

The paper identifies distinct neural modes in the sensorimotor cortex that encode grasp force and timing during isometric grasps in tetraplegic individuals.

Stabilizing brain-computer interfaces through alignment of latent dynamics

This paper introduces NoMAD, a method that stabilizes brain-computer interfaces by aligning latent dynamics, achieving stable decoding without supervised recalibration over long periods.

Less is more: selection from a small set of options improves BCI velocity control

This paper introduces a discrete direction selection decoder for iBCI that outperforms continuous velocity decoders in human and monkey experiments by improving control accuracy and reducing noise.

Feedback-based motor control can guide plasticity and drive rapid learning

This study shows that feedback-based motor control and motor adaptation share a common implementation in M1 circuits, as demonstrated through a recurrent neural network trained with error feedback.

Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface

This paper develops an unsupervised piecewise linear decoder for multi-task brain-computer interfaces, showing it outperforms global decoders in EMG prediction.

A Generalist Intracortical Motor Decoder

The paper trains an autoregressive Transformer on neural spiking data to decode motor behavior, showing broad utility across tasks but highlighting limitations in scaling.

Biomimetic multi-channel microstimulation of somatosensory cortex conveys high resolution force feedback for bionic hands

This study evaluates biomimetic multi-channel microstimulation for restoring high-resolution force feedback in bionic hands through somatosensory cortex stimulation.

Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders

The paper develops a topographic autoencoder to model proprioceptive cortical coding and predicts neural manifold structures from natural movement data.

Motor cortex latent dynamics encode spatial and temporal arm movement parameters independently

The paper shows that motor cortex activity encodes spatial and temporal arm movement parameters independently through low-dimensional neural trajectories.

BRAND: A platform for closed-loop experiments with deep network models

BRAND provides a platform for closed-loop experiments with deep networks, achieving low latency and supporting multiple programming languages.

Task-driven neural network models predict neural dynamics of proprioception

The paper uses task-driven neural networks to predict neural activity in proprioceptive pathways during reaching tasks, finding that task-optimized models outperform other models.

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.

Microstimulation of human somatosensory cortex evokes task-dependent, spatially patterned responses in motor cortex

This study investigates how microstimulation of somatosensory cortex affects motor cortex activity in paralyzed individuals, finding task-dependent spatial patterns and showing that biomimetic stimulation improves control of a virtual hand.

Preserved neural population dynamics across animals performing similar behaviour

The paper shows that neural population dynamics are preserved across individuals of the same species performing similar behaviours, even when the brain structures differ.

Monkey-to-human transfer of brain computer interface decoders

This paper transfers brain-computer interface decoders from monkeys to humans, showing that motor-related neural representations are consistent across species and reducing the need for extensive data.

Using adversarial networks to extend brain computer interface decoding accuracy over time

This paper improves brain-computer interface decoding accuracy over time using Cycle-GAN and ADAN methods, outperforming linear methods with minimal data requirements.

Nonlinear manifolds underlie neural population activity during behaviour

Across monkey, mouse and human motor cortex and mouse striatum, population activity is reported to occupy curved rather than flat low-dimensional spaces, most clearly in complex tasks.

Tessellation Of Artificial Touch Via Microstimulation Of Human Somatosensory Cortex

The paper investigates the localization of intracortical microstimulation-evoked sensations in human somatosensory cortex and demonstrates improved localization with multi-channel stimulation in a bionic hand.

Low-dimensional neural manifolds for the control of constrained and unconstrained movements

The paper investigates whether low-dimensional neural manifolds in M1 are present during unconstrained natural movements and finds they are, with similar dimensionality to constrained movements.

De novo motor learning creates structure in neural activity space that shapes adaptation

The paper shows that neural activity structure from prior learning influences adaptation, with more structured networks adapting better under specific conditions.

A large-scale neural network training framework for generalized estimation of single-trial population dynamics

This paper introduces AutoLFADS, an automated framework for estimating neural population dynamics from neural data without supervisory information, validated on multiple datasets.

Small, correlated changes in synaptic connectivity may facilitate rapid motor learning

The paper uses a network model to show that small, correlated changes in synaptic connectivity can explain motor learning without disrupting neural covariance, aligning with experimental observations.

Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner

The paper examines the relationship between local field potentials (LFPs) and latent dynamics in primate sensorimotor cortex, finding frequency-dependent and region-specific correlations.