Dario Farina

This paper introduces an adaptive EMG decomposition algorithm for real-time motor neuron discharge identification during dynamic movements, showing improved performance in simulated and experimental data.

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.

This paper shows that larger and denser EMG electrode grids improve the identification of more representative motor unit samples through computational and experimental analysis.

The paper identifies two motor neuron synergies in triceps surae muscles during plantarflexion that remain stable across ankle angles.

This study investigates high-frequency cortical inputs to muscles during movement cancellation, finding increased beta and gamma oscillations in brain and muscle activity during NO-GO trials without behavioral changes.

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

The paper identifies Err2 and Err3 as transcription factors promoting functional properties in gamma motor neurons essential for proprioceptive movement control.

The paper proposes a method combining EMG signal decomposition and realistic motoneuron modeling to estimate the firing behavior of entire motoneuron pools in humans.

The paper tracks motor units in non-human primates during force contractions, finding stable recruitment order and a dominant neural component for force control.

The paper derives mathematical relationships between electrophysiological and anatomical properties of spinal alpha-motoneurons across species, validating existing theories and providing a tool for modeling and experimental analysis.

The paper shows that humans cannot easily control individual motor units independently, as their activation is governed by a shared input signal and changes in de-recruitment order.

The paper develops a high-density electrode and methodology to decode up to 67 concurrently active motoneurons in humans with 99% accuracy, revealing population-level synaptic input and inhibition patterns.

The study identifies two neural modules underlying motor unit discharge rates in synergistic muscles, showing heterogeneous distribution of common synaptic inputs among motor neurons.

This study investigates whether TACS modulates corticospinal beta entrainment in humans using simulations and electromyography, finding no consistent effect.

The paper identifies common synaptic inputs to spinal motor neurons during a multi-joint task, revealing neural synergies that partially decouple from muscle innervation.

This paper describes a novel experimental model for double nerve transfers to a single target muscle in rats, showing successful reinnervation without functional deficits.

This paper proposes a control framework for human-robot interaction that robustly predicts user movement intention, surpassing manual control in accuracy and usability.

The paper assesses inter-operator reliability in decomposing HDsEMG into motor unit spike trains, finding high agreement across operators.

This paper presents a non-invasive wrist wearable interface that decodes spinal motoneuron activity to predict finger movements with high accuracy in real-time.

This study shows that isometric strength training increases maximal force but not rate of force development (RFD) due to unchanged motor unit recruitment speed, despite increased discharge rates during plateau phases.

The paper introduces improved methods to measure corticospinal transmission delays of beta rhythms and shows they align with fastest axon speeds, suggesting two possible mechanisms for fast transmission.