Interfacing the neural output of the spinal cord: robust and reliable longitudinal identification of motor neurons in humans
Author(s) -
Alessandro Del Vecchio,
Dario Farina
Publication year - 2019
Publication title -
journal of neural engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.594
H-Index - 111
eISSN - 1741-2560
pISSN - 1741-2552
DOI - 10.1088/1741-2552/ab4d05
Subject(s) - interfacing , computer science , neural coding , neuroscience , motor neuron , neurophysiology , artificial intelligence , spinal cord , motor system , artificial neural network , motor unit , pattern recognition (psychology) , computer hardware , biology
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.
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