Non-invasive analysis of motor neurons controlling the intrinsic and extrinsic muscles of the hand
Journal article, 2020

Objective. We present a non-invasive framework for investigating efferent commands to 14 extrinsic and intrinsic hand muscles. We extend previous studies (limited to a few muscles) on common synaptic input among pools of motor neurons in a large number of muscles.Approach. Seven subjects performed sinusoidal isometric contractions to complete seven types of grasps, with each finger and with three combinations of fingers in opposition with the thumb. High-density surface EMG (HD-sEMG) signals (384 channels in total) recorded from the 14 muscles were decomposed into the constituent motor unit action potentials. This provided a non-invasive framework for the investigation of motor neuron discharge patterns, muscle coordination and efferent commands of the hand muscles during grasping. Moreover, during grasping tasks, it was possible to identify common neural information among pools of motor neurons innervating the investigated muscles. For this purpose, principal component analysis (PCA) was applied to the smoothed discharge rates of the decoded motor units.Main results. We found that the first principal component (PC1) of the ensemble of decoded motor neuron spike trains explained a variance of (53.0 +/- 10.9) % and was positively correlated with force (R = 0.67 +/- 0.10 across all subjects and tasks). By grouping the pools of motor neurons from extrinsic or intrinsic muscles, the PC1 explained a proportion of variance of (57.1 +/- 11.3) % and (56.9 +/- 11.8) %, respectively, and was correlated with force with R = 0.63 +/- 0.13 and 0.63 +/- 0.13, respectively.Significance.These observations demonstrate a low dimensional control of motor neurons across multiple muscles that can be exploited for extracting control signals in neural interfacing. The proposed framework was designed for hand rehabilitation perspectives, such as post-stroke rehabilitation and hand-exoskeleton control.


hand muscles

motor units

neural drive

neural interface


Simone Tanzarella

Imperial College London

Silvia Muceli

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Biomedical Signals and Systems

Alessandro Del Vecchio

Imperial College London

Andrea Casolo

Imperial College London

Dario Farina

Imperial College London

Journal of Neural Engineering

1741-2560 (ISSN)

Vol. 17 4 046033

Subject Categories

Sport and Fitness Sciences







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