Biology subjects

Formento, E.

Publications and source records attributed to Formento, E..

4 recordsLinked to original sources

bioRxiv2026

Enabling Skilled Human-Computer Interaction After Paralysis via a Wearable sEMG Interface

Most individuals with tetraplegia retain some myoelectric function in their forearms, which offers the possibility of using surface electromyographic (sEMG) control for human-computer interaction (HCI). We demonstrate the potential of this approach by showing that people with motor-complete (n=5) and motor-incomplete (n=2) tetraplegia can accurately control myoelectric activity in their forearm to perform discrete button-click and continuous positioning tasks. These control inputs were mapped to the firing rate of motor units detected by a wireless wristband sensor designed for everyday use. Participants completed four testing sessions to assess their speed and accuracy. Motor units that displayed a wide dynamic range in their firing rate performed best during tasks requiring continuous, single-axis control. Interestingly, the level of impairment did not affect performance on the clicking and 1D cursor control tasks. However, those with motor-incomplete injuries showed greater independent control over two motor units than participants with motor-complete injuries, who exhibited stronger coupling between units. Participants also confirmed the practical utility of the device, successfully placing and removing the sEMG wristband on their own and consistently rating it as comfortable and easy to manage. These findings are significant because they offer the first demonstration of motor unit-based control in individuals with cervical spinal cord injury (SCI) using a fully wearable wristband interface, highlighting the feasibility of moving these systems out of the lab and into daily life. One-Sentence SummaryPeople with tetraplegia used a wristband sensor to detect forearm motor unit firing and perform human-computer interaction tasks.

bioengineering↗

bioRxiv2021

A non-invasive brain-machine interface via independent control of individual motor units

Brain-machine interfaces (BMIs) have the potential to augment human functions and restore independence in people with disabilities, yet a compromise between non-invasiveness and performance limits their relevance. Here, we demonstrate a BMI controlled by individual motor units non-invasively recorded from the biceps brachii. Through real-time auditory and visual neurofeedback of motor unit activity, 8 participants learned to skillfully and independently control three motor units in order to complete a two-dimensional center-out task, with marked improvements in control over 6 days of training. Concomitantly, dimensionality of the motor unit population increased significantly relative to naturalistic behaviors, largely violating recruitment orders displayed during stereotyped, isometric muscle contractions. Finally, participants demonstrated the potential of a motor unit BMI to power general applications by navigating a virtual keyboard in a spelling task, achieving performances comparable to spelling-tailored non-invasive BMIs that leverage less flexible control strategies to improve performance. These results highlight a largely unexplored level of flexibility of the sensorimotor system and show that this can be exploited to create a versatile, skillfully-controllable non-invasive BMI that has great potential to both provide translational benefit and augment motor functions.

bioengineering↗

bioRxiv2020

Experimental and computational study on motor control and recovery after stroke: towards a constructive loop betweenexperimental and virtual embodiedneuroscience

Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.

neuroscience↗

bioRxiv2019

A biomimetic electrical stimulation strategy to induce asynchronous stochastic neural activity

Electrical stimulation is an effective method to communicate with the nervous system. Here, we demonstrate that current stimulation paradigms fail to reproduce the stochastic and asynchronous properties of natural patterns of neural activity, and introduce a novel biomimetic stimulation (BioS) strategy that overcomes these limitations. We hypothesized that high-frequency amplitude-modulated bursts of stimulation could induce asynchronous neural firings by distributing recruitment over the duration of a burst, without sacrificing the ability to precisely control neural activity. We tested this hypothesis using computer simulations and ex vivo experiments. We found that BioS bursts induce asynchronous, stochastic, yet controllable, neural activity. We established that varying the amplitude, duration, and repetition frequency of a BioS burst enables graded modulation of the number of recruited fibers, their firing rate, and the synchronicity of the responses. These results demonstrate an unprecedented level of control over artificially induced neural activity, enabling the design of next-generation biomimetic stimulation paradigms with potentially profound consequences for the field of neurostimulation.

bioengineering↗