Publications associées à Nerve conduction study

Early decoding of walking tasks with minimal set of EMG channels

Silvestro Micera, Francesco Iberite, Federica Barberi, Eugenio Anselmino

Objective. Powered lower-limb prostheses relying on decoding motor intentions from non-invasive sensors, like electromyographic (EMG) signals, can significantly improve the quality of life of amputee subjects. However, the optimal combination of high decod ...

IOP Publishing Ltd2023

Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes

Silvestro Micera, Simone Romeni, Elena Losanno, Luca Pierantoni

Objective. Peripheral nerve interfaces have the potential to restore sensory, motor, and visceral functions. In particular, intraneural interfaces allow targeting deep neural structures with high selectivity, even if their performance strongly depends upon ...

IOP Publishing Ltd2023

Machine-learning predictor of nerve fiber firing rate allows the automatic optimization of electrical stimulation protocols

Silvestro Micera, Simone Romeni, Luca Pierantoni, Gabriele Marino

Computational models have been widely employed to study the electrical stimulation of the nervous system. Still, most applications either study fundamental mechanisms underlying stimulation, or address qualitative scientific questions. When quantitative qu ...

IEEE2023

DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies

Friedhelm Christoph Hummel, Claudia Bigoni, Nima Taherinejad

The analysis of motor evoked potentials (MEPs) generated by transcranial magnetic stimulation (TMS) is crucial in research and clinical medical practice. MEPs are characterized by their latency and the treatment of a single patient may require the characte ...

NATURE PORTFOLIO2023

Computational approaches to decode grasping force and velocity level in upper-limb amputee from intraneural peripheral signals

Silvestro Micera, Ivo Strauss, Giacomo Valle

Objective. Recent results have shown the potentials of neural interfaces to provide sensory feedback to subjects with limb amputation increasing prosthesis usability. However, their advantages for decoding motor control signals over current methods based o ...

IOP Publishing Ltd2021

Hybrid Human-Machine Interface for Gait Decoding Through Bayesian Fusion of EEG and EMG Classifiers

Silvestro Micera, Fiorenzo Artoni, Luca Tonin, Stefano Tortora

Despite the advances in the field of brain computer interfaces (BCI), the use of the sole electroencephalography (EEG) signal to control walking rehabilitation devices is currently not viable in clinical settings, due to its unreliability. Hybrid interface ...

2020

Tutorial: a computational framework for the design and optimization of peripheral neural interfaces

Silvestro Micera, Simone Romeni, Giacomo Valle

Peripheral neural interfaces have been successfully used in the recent past to restore sensory-motor functions in disabled subjects and for the neuromodulation of the autonomic nervous system. The optimization of these neural interfaces is crucial for ethi ...

NATURE RESEARCH2020

Characterization of multi-channel intraneural stimulation in transradial amputees

Silvestro Micera, Stanisa Raspopovic, Fiorenzo Artoni, Francesco Maria Petrini, Ivo Strauss, Giacomo Valle, Edoardo D'Anna

Although peripheral nerve stimulation using intraneural electrodes has been shown to be an effective and reliable solution to restore sensory feedback after hand loss, there have been no reports on the characterization of multi-channel stimulation. A deepe ...

Springer2019

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Nerve conduction study

Early decoding of walking tasks with minimal set of EMG channels

Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes

Machine-learning predictor of nerve fiber firing rate allows the automatic optimization of electrical stimulation protocols

DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies

Prevention of the foreign body response to implantable medical devices by inflammasome inhibition

Computational approaches to decode grasping force and velocity level in upper-limb amputee from intraneural peripheral signals

Hybrid Human-Machine Interface for Gait Decoding Through Bayesian Fusion of EEG and EMG Classifiers

Tutorial: a computational framework for the design and optimization of peripheral neural interfaces

Cognitive neuroprosthetics for investigating and restoring body perception

Characterization of multi-channel intraneural stimulation in transradial amputees

Graph Chatbot

Chattez avec Graph Search

Early decoding of walking tasks with minimal set of EMG channels

Tutorial: a computational framework for the design and optimization of peripheral neural interfaces

Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes

Characterization of multi-channel intraneural stimulation in transradial amputees

Prevention of the foreign body response to implantable medical devices by inflammasome inhibition

Cognitive neuroprosthetics for investigating and restoring body perception

DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies

Hybrid Human-Machine Interface for Gait Decoding Through Bayesian Fusion of EEG and EMG Classifiers

Computational approaches to decode grasping force and velocity level in upper-limb amputee from intraneural peripheral signals

Machine-learning predictor of nerve fiber firing rate allows the automatic optimization of electrical stimulation protocols