Microneurography as a tool to develop decoding algorithms for peripheral neuro-controlled hand prostheses

Background The usability of dexterous hand prostheses is still hampered by the lack of natural and effective control strategies. A decoding strategy based on the processing of descending efferent neural signals recorded using peripheral neural interfaces could be a solution to such limitation. Unfor...

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Veröffentlicht in:Biomedical engineering online Jg. 18; H. 1; S. 44 - 22
Hauptverfasser: Petrini, Francesco M., Mazzoni, Alberto, Rigosa, Jacopo, Giambattistelli, Federica, Granata, Giuseppe, Barra, Beatrice, Pampaloni, Alessandra, Guglielmelli, Eugenio, Zollo, Loredana, Capogrosso, Marco, Micera, Silvestro, Raspopovic, Stanisa
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 08.04.2019
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Algorithms
Amputation
Amputees
Artificial arms
Biomaterials
Biomedical Engineering and Bioengineering
Biomedical Engineering/Biotechnology
Biotechnology
Care and treatment
Computational neuroscience
Computer applications
Decoding
Design and construction
Dextrous hands
Electrodes
Engineering
Feedback
Hand
Health aspects
Hypodermic needles
Information processing
Interfaces
Invasive diagnosis
Mathematical models
Methods
Microneurography
Motor control
Motor neurons
Needles
Nerves
Nervous system
Neural prostheses
Neurophysiology
Neuroprosthetics
Neurosciences
Physiology
Prostheses
Prosthetics
Recording
Signal processing
Technology application
Ultrasonic imaging
Ultrasound
Switzerland
Neuroprosthetics
Microneurography
Decoding
Motor control
Amputation
ISSN:1475-925X, 1475-925X
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Zusammenfassung:Background The usability of dexterous hand prostheses is still hampered by the lack of natural and effective control strategies. A decoding strategy based on the processing of descending efferent neural signals recorded using peripheral neural interfaces could be a solution to such limitation. Unfortunately, this choice is still restrained by the reduced knowledge of the dynamics of human efferent signals recorded from the nerves and associated to hand movements. Findings To address this issue, in this work we acquired neural efferent activities from healthy subjects performing hand-related tasks using ultrasound-guided microneurography, a minimally invasive technique, which employs needles, inserted percutaneously, to record from nerve fibers. These signals allowed us to identify neural features correlated with force and velocity of finger movements that were used to decode motor intentions. We developed computational models, which confirmed the potential translatability of these results showing how these neural features hold in absence of feedback and when implantable intrafascicular recording, rather than microneurography, is performed. Conclusions Our results are a proof of principle that microneurography could be used as a useful tool to assist the development of more effective hand prostheses.
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ISSN:1475-925X
1475-925X
DOI:10.1186/s12938-019-0659-9