Lee E. Fisher

The study tests if a crossmodal congruency task can measure the intuitiveness of sensory feedback in lower limb prostheses, finding it effective at the knee but not the foot.

This study evaluates how electrode size and stimulation configurations affect nerve selectivity in spinal cord stimulation, finding that 500 µm electrodes provide the best selectivity.

This study shows that cervical spinal cord stimulation impairs proprioception but improves voluntary arm reaching in post-stroke patients.

This study shows that lateral spinal cord stimulation (LSCS) selectivity is limited by the coarse organization of lumbar dorsal rootlets, not electrode design, and suggests improving electrode placement may enhance percept localization.

This paper introduces SpIC3D, a 3D imaging method for high-resolution visualization of spinal structures, enabling quantification of neuronal density and volumetric reconstruction for computational modeling.

This study identifies and characterizes sources of artifact contaminating evoked compound action potentials (ECAPs) in epidural spinal cord recordings, highlighting challenges in distinguishing neural signals from artifacts in a swine model.

This paper introduces a new electrical stimulation waveform that evokes more naturalistic neural activity than traditional methods, as shown by human experiments and computational modeling.

This study shows that general anesthesia reduces gastric myoelectric power in ferrets and that behavioral movement increases signal power compared to rest.

This study demonstrates selective cervical vagus nerve stimulation using flexible cuff electrodes to target abdominal pathways while minimizing cardiovascular side effects in a rat model.

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.

HSST optimizes spike sorting parameters using neurophysiological metrics, improving sorting accuracy and robustness across varying conditions.

This study uses machine learning to predict emesis and gastrointestinal states in ferrets using implanted myoelectric signals.