Muscle synergy analysis investigates the neurophysiological mechanisms that the central nervous system employs to coordinate muscles. Several models have been developed to decompose electromyographic (EMG) signals into spatial and temporal synergies. However, using multiple approaches can complicate the inter-pretation of results. Spatial synergies represent invariant muscle weights modulated with variant temporal coef-ficients; temporal synergies are invariant temporal profiles that coordinate variant muscle weights. While non-negative matrix factorization allows to extract both spatial and temporal synergies, the comparison between the two approaches was rarely investigated targeting a large set of multi-joint upper-limb movements. Spatial and temporal synergies were extracted from two datasets with proximal (16 subjects, 10M, 6F) and distal upper-limb movements (30 subjects, 21M, 9F), focusing on their differences in reconstruction accuracy and inter-individual variability. We showed the existence of both spatial and temporal structure in the EMG data, com-paring synergies with those from a surrogate dataset in which the phases were shuffled preserving the frequency content of the original data. The two models provide a compact characterization of motor coordination at the spa-tial or temporal level, respectively. However, a lower number of temporal synergies are needed to achieve the same reconstruction R2: spatial and temporal synergies may capture different hierarchical levels of motor control and are dual approaches to the characterization of low-dimensional coordination of the upper-limb. Last, a detailed characterization of the structure of the temporal synergies suggested that they can be related to intermit-tent control of the movement, allowing high flexibility and dexterity. These results improve neurophysiology understanding in several fields such as motor control, rehabilitation, and prosthetics.(c) 2023 IBRO. Published by Elsevier Ltd. All rights reserved.
Spatial and Temporal Muscle Synergies Provide a Dual Characterization of Low-dimensional and Intermittent Control of Upper-limb Movements
Brambilla Cristina;Scano Alessandro
2023
Abstract
Muscle synergy analysis investigates the neurophysiological mechanisms that the central nervous system employs to coordinate muscles. Several models have been developed to decompose electromyographic (EMG) signals into spatial and temporal synergies. However, using multiple approaches can complicate the inter-pretation of results. Spatial synergies represent invariant muscle weights modulated with variant temporal coef-ficients; temporal synergies are invariant temporal profiles that coordinate variant muscle weights. While non-negative matrix factorization allows to extract both spatial and temporal synergies, the comparison between the two approaches was rarely investigated targeting a large set of multi-joint upper-limb movements. Spatial and temporal synergies were extracted from two datasets with proximal (16 subjects, 10M, 6F) and distal upper-limb movements (30 subjects, 21M, 9F), focusing on their differences in reconstruction accuracy and inter-individual variability. We showed the existence of both spatial and temporal structure in the EMG data, com-paring synergies with those from a surrogate dataset in which the phases were shuffled preserving the frequency content of the original data. The two models provide a compact characterization of motor coordination at the spa-tial or temporal level, respectively. However, a lower number of temporal synergies are needed to achieve the same reconstruction R2: spatial and temporal synergies may capture different hierarchical levels of motor control and are dual approaches to the characterization of low-dimensional coordination of the upper-limb. Last, a detailed characterization of the structure of the temporal synergies suggested that they can be related to intermit-tent control of the movement, allowing high flexibility and dexterity. These results improve neurophysiology understanding in several fields such as motor control, rehabilitation, and prosthetics.(c) 2023 IBRO. Published by Elsevier Ltd. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.