D. Farina

Identification of motor unit discharges from ultrasound images: Analysis of in silico and in vivo experiments

This study evaluates decoding algorithms for identifying motor unit discharges from ultrasound images under non-ideal conditions, finding they remain accurate despite varying twitch shapes and fusion.

NeuroMotion: Open-source Simulator with Neuromechanical and Deep Network Models to Generate Surface EMG signals during Voluntary Movement

NeuroMotion generates synthetic EMG signals during voluntary movements by integrating musculoskeletal models, neural networks, and motor unit models, enabling analysis of neuromechanical interactions.

Accurate Identification of Motoneuron Discharges from Ultrasound Images Across the Full Muscle Cross-Section

This paper develops a method to accurately identify motoneuron discharges from ultrasound images across the full muscle cross-section, achieving high agreement with surface EMG decomposition.

Measuring and monitoring skill learning in closed-loop myoelectric hand prostheses using speed-accuracy tradeoffs

This study measures skill learning in closed-loop myoelectric prostheses using speed-accuracy tradeoffs to monitor progress and inform interface optimization.

Spatial decomposition of ultrafast ultrasound images to identify motor unit activity - A validation study using intramuscular and surface EMG

This study validates blind source separation methods on ultrafast ultrasound images to identify motor unit activity using EMG data from eleven participants.

Motoneuron-driven computational muscle modelling with motor unit resolution and subject-specific human musculoskeletal anatomy

This paper develops a motoneuron-driven neuromuscular model that simulates human muscle contraction with motor unit resolution and subject-specific anatomy, validated against experimental force data.

Non-linearity in motor unit velocity twitch dynamics: Implications for ultrafast ultrasound source separation

The paper investigates non-linear dynamics in motor unit velocity twitch profiles using ultrasound data, showing that linear models are insufficient for accurate decomposition.

A graph-based approach to identify motor neuron synergies

This paper introduces a graph-based method to estimate the number and strength of synaptic inputs to motor neurons using discharge rate correlations, validated through simulations and applied to experimental data.

Low-frequency motor cortex EEG predicts four levels of rate of change of force during ankle dorsiflexion

This paper uses EEG signals from the motor cortex to classify four levels of force development rate during ankle dorsiflexion with 82% accuracy using time-frequency features.

Non-invasive muscle biopsy: estimation of muscle fibre size from a neuromuscular interface

This study establishes that motor unit conduction velocity can non-invasively estimate muscle fibre size in humans with moderate accuracy.

Kinematics of individual muscle units in natural contractions measured in vivo using ultrafast ultrasound

This study measures excitation-contraction coupling in individual muscle units during natural contractions using HDsEMG and ultrafast ultrasound.

Neural decoding from surface high-density EMG signals: influence of anatomy and synchronization on the number of identified motor units

This study investigates how anatomical and neural factors affect the number of motor units identified from HD-sEMG signals during isometric contractions.

Towards the Myoelectric Digital Twin: Ultra Fast and Realistic Modelling for Deep Learning

This paper introduces a highly realistic and ultra-fast computational model for simulating electromyography signals to train deep learning algorithms, enabling data augmentation and improved decoding of muscular signals.