Jennifer L. Collinger

This study examines how attentional load affects intracortical brain-computer interface (iBCI) performance during dual-tasking, finding robust performance but individual variability.

This study decodes motor cortex activity in real-time to predict movement onset before action, showing a task-independent commitment to act that aligns with conscious intent.

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.

This paper introduces error modulation in motor BCIs using pre-error motor cortex activity to improve cursor control performance during online BCI tasks.

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

This study uses TMS and fMRI to investigate how non-primary motor areas affect reaching performance, finding that PMd and PPC perturbations alter movement smoothness and accuracy.

People with tetraplegia used a wristband sensor to detect forearm motor unit firing and perform human-computer interaction tasks.

The paper uses a finite element model to predict tissue strain from micromotion in Utah arrays and correlates these strains with electrode performance in humans and non-human primates.

This study examines how immersive environments affect BCI performance, finding that prior experience with an environment is more important than its immersiveness for decoder performance and generalization.

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

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

The paper shows that motor cortex maintains similar neural dynamics during motor execution and imagery by recreating missing components in a unique imagery subspace.

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

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.

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.

NDT2 pretrains a spatiotemporal Transformer on multi-session motor BCI data to improve decoding performance across novel contexts.

This study investigates how microstimulation frequency affects perceptual responses in human somatosensory cortex, finding spatially organized effects on perceptual intensity and quality.

The paper validates and scales musculoskeletal models of the human forearm and hand using posture-dependent moment arms, isometric force measurements, and error detection to improve accuracy in torque prediction.