Imperial College London
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ProfessorDarioFarina

Faculty of Engineering, Department of Bioengineering

Chair in Neurorehabilitation Engineering
 
 
 
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Contact

 

+44 (0)20 7594 1387d.farina Website

 
 
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Location

 

RSM 4.15Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Del:2019:1741-2552/ab4d05,
author = {Del, Vecchio A and Farina, D},
doi = {1741-2552/ab4d05},
journal = {Journal of Neural Engineering},
pages = {1--11},
title = {Interfacing the neural output of the spinal cord: robust and reliable longitudinal identification of motor neurons in humans},
url = {http://dx.doi.org/10.1088/1741-2552/ab4d05},
volume = {17},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Objective. Non-invasive electromyographic techniques can detect action potentials from muscle units with high spatial dimensionality. These technologies allow the decoding of large samples of motor units by using high-density grids of electrodes that are placed on the skin overlying contracting muscles and therefore provide a non-invasive representation of the human spinal cord output. Approach. From a sample of  >1200 decoded motor neurons, we show that motor neuron activity can be identified in humans in the full muscle recruitment range with high accuracy. Main results. After showing the validity of decomposition with novel test parameters, we demonstrate that the same motor neurons can be tracked over a period of one-month, which allows for the longitudinal analysis of individual human neural cells. Significance. These results show the potential of an accurate and reliable assessment of large populations of motor neurons in physiological investigations. We discuss the potential of this non-invasive neural interfacing technology for the study of the neural determinants of movement and man-machine interfacing.
AU  - Del,Vecchio A
AU  - Farina,D
DO  - 1741-2552/ab4d05
EP  - 11
PY  - 2019///
SN  - 1741-2552
SP  - 1
TI  - Interfacing the neural output of the spinal cord: robust and reliable longitudinal identification of motor neurons in humans
T2  - Journal of Neural Engineering
UR  - http://dx.doi.org/10.1088/1741-2552/ab4d05
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000537460300003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://iopscience.iop.org/article/10.1088/1741-2552/ab4d05
UR  - http://hdl.handle.net/10044/1/81680
VL  - 17
ER  -
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