Microneurography as a tool to develop decoding algorithms for peripheral neuro-controlled hand prostheses
Abstract 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 limitati...
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2019-04-01
|
| Series: | BioMedical Engineering OnLine |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12938-019-0659-9 |
| _version_ | 1818174698458972160 |
|---|---|
| author | Francesco M. Petrini Alberto Mazzoni Jacopo Rigosa Federica Giambattistelli Giuseppe Granata Beatrice Barra Alessandra Pampaloni Eugenio Guglielmelli Loredana Zollo Marco Capogrosso Silvestro Micera Stanisa Raspopovic |
| author_facet | Francesco M. Petrini Alberto Mazzoni Jacopo Rigosa Federica Giambattistelli Giuseppe Granata Beatrice Barra Alessandra Pampaloni Eugenio Guglielmelli Loredana Zollo Marco Capogrosso Silvestro Micera Stanisa Raspopovic |
| author_sort | Francesco M. Petrini |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-11T19:48:32Z |
| format | Article |
| id | doaj.art-8815850e84b041c79544546cf48d6a25 |
| institution | Directory Open Access Journal |
| issn | 1475-925X |
| language | English |
| last_indexed | 2024-12-11T19:48:32Z |
| publishDate | 2019-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj.art-8815850e84b041c79544546cf48d6a252022-12-22T00:52:51ZengBMCBioMedical Engineering OnLine1475-925X2019-04-0118112210.1186/s12938-019-0659-9Microneurography as a tool to develop decoding algorithms for peripheral neuro-controlled hand prosthesesFrancesco M. Petrini0Alberto Mazzoni1Jacopo Rigosa2Federica Giambattistelli3Giuseppe Granata4Beatrice Barra5Alessandra Pampaloni6Eugenio Guglielmelli7Loredana Zollo8Marco Capogrosso9Silvestro Micera10Stanisa Raspopovic11Neuroengineering Lab, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH ZürichThe BioRobotics Institute, Scuola Superiore Sant’AnnaCenter for Neuroprosthetics, Ecole Polytechnique Federale de LausanneInstitute of Neurology, Università Campus Bio-Medico di RomaIRCCS S.Raffale-PisanaCenter for Neuroprosthetics, Ecole Polytechnique Federale de LausanneCenter for Neuroprosthetics, Ecole Polytechnique Federale de LausanneLaboratory of Biomedical Robotics & Biomicrosystems, Università Campus Bio-Medico di RomaLaboratory of Biomedical Robotics & Biomicrosystems, Università Campus Bio-Medico di RomaCenter for Neuroprosthetics, Ecole Polytechnique Federale de LausanneCenter for Neuroprosthetics, Ecole Polytechnique Federale de LausanneNeuroengineering Lab, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH ZürichAbstract 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.http://link.springer.com/article/10.1186/s12938-019-0659-9MicroneurographyAmputationNeuroprostheticsMotor controlDecoding |
| spellingShingle | Francesco M. Petrini Alberto Mazzoni Jacopo Rigosa Federica Giambattistelli Giuseppe Granata Beatrice Barra Alessandra Pampaloni Eugenio Guglielmelli Loredana Zollo Marco Capogrosso Silvestro Micera Stanisa Raspopovic Microneurography as a tool to develop decoding algorithms for peripheral neuro-controlled hand prostheses BioMedical Engineering OnLine Microneurography Amputation Neuroprosthetics Motor control Decoding |
| title | Microneurography as a tool to develop decoding algorithms for peripheral neuro-controlled hand prostheses |
| title_full | Microneurography as a tool to develop decoding algorithms for peripheral neuro-controlled hand prostheses |
| title_fullStr | Microneurography as a tool to develop decoding algorithms for peripheral neuro-controlled hand prostheses |
| title_full_unstemmed | Microneurography as a tool to develop decoding algorithms for peripheral neuro-controlled hand prostheses |
| title_short | Microneurography as a tool to develop decoding algorithms for peripheral neuro-controlled hand prostheses |
| title_sort | microneurography as a tool to develop decoding algorithms for peripheral neuro controlled hand prostheses |
| topic | Microneurography Amputation Neuroprosthetics Motor control Decoding |
| url | http://link.springer.com/article/10.1186/s12938-019-0659-9 |
| work_keys_str_mv | AT francescompetrini microneurographyasatooltodevelopdecodingalgorithmsforperipheralneurocontrolledhandprostheses AT albertomazzoni microneurographyasatooltodevelopdecodingalgorithmsforperipheralneurocontrolledhandprostheses AT jacoporigosa microneurographyasatooltodevelopdecodingalgorithmsforperipheralneurocontrolledhandprostheses AT federicagiambattistelli microneurographyasatooltodevelopdecodingalgorithmsforperipheralneurocontrolledhandprostheses AT giuseppegranata microneurographyasatooltodevelopdecodingalgorithmsforperipheralneurocontrolledhandprostheses AT beatricebarra microneurographyasatooltodevelopdecodingalgorithmsforperipheralneurocontrolledhandprostheses AT alessandrapampaloni microneurographyasatooltodevelopdecodingalgorithmsforperipheralneurocontrolledhandprostheses AT eugenioguglielmelli microneurographyasatooltodevelopdecodingalgorithmsforperipheralneurocontrolledhandprostheses AT loredanazollo microneurographyasatooltodevelopdecodingalgorithmsforperipheralneurocontrolledhandprostheses AT marcocapogrosso microneurographyasatooltodevelopdecodingalgorithmsforperipheralneurocontrolledhandprostheses AT silvestromicera microneurographyasatooltodevelopdecodingalgorithmsforperipheralneurocontrolledhandprostheses AT stanisaraspopovic microneurographyasatooltodevelopdecodingalgorithmsforperipheralneurocontrolledhandprostheses |