Synergistic recruitment of multi-scale myoelectric oscillations of upper limb during infant crawling

It has been widely accepted that the central nervous system (CNS) modulates muscle synergies to simplify motion control. However, it is still unclear that if there is a synergistic recruitment strategy to organize oscillation components of surface electromyography (sEMG) signals for limb movement. The sEMG signals were recorded from bilateral biceps brachii (BB) and triceps brachii (TB) muscles during infant crawling. The multivariate empirical mode decomposition (MEMD) was applied to decompose multi-channel sEMG signals into multi-scale oscillations. Then, non-negative matrix factorization (NMF) method was employed to extract oscillation synergy patterns. The results indicated that there were three stable oscillation synergies in sEMG signals for crawling movement, and the recruitment coefficient curves reflected the role of muscle during crawling movement. Our preliminary work suggested that synergistic recruitment of multi-scale oscillation components maybe a new way to understand the organization of MU recruitment strategy by the CNS.

[1]  Joshua C Kline,et al.  Decomposition of surface EMG signals from cyclic dynamic contractions. , 2015, Journal of neurophysiology.

[2]  J. Foley The co-ordination and regulation of movements , 1968 .

[3]  B. Nigg,et al.  Changes in EMG signals for the muscle tibialis anterior while running barefoot or with shoes resolved by non-linearly scaled wavelets. , 2003, Journal of biomechanics.

[4]  Kristian Gundersen,et al.  Excitation-transcription coupling in skeletal muscle: the molecular pathways of exercise , 2011, Biological reviews of the Cambridge Philosophical Society.

[5]  Karl M Newell,et al.  Amplitude changes in the 8–12, 20–25, and 40 Hz oscillations in finger tremor , 2000, Clinical Neurophysiology.

[6]  James M. Wakeling,et al.  A Muscle’s Force Depends on the Recruitment Patterns of Its Fibers , 2012, Annals of Biomedical Engineering.

[7]  D. P. Mandic,et al.  Multivariate empirical mode decomposition , 2010, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[8]  Patricia Dolan,et al.  Back extensor muscle fatigue at submaximal workloads assessed using frequency banding of the electromyographic signal. , 2011, Clinical biomechanics.

[9]  E. Christou,et al.  Increased voluntary drive is associated with changes in common oscillations from 13 to 60 Hz of interference but not rectified electromyography , 2010, Muscle & nerve.

[10]  Chen-Hua Yeow,et al.  Comparison of mean frequency and median frequency in evaluating muscle fiber type selection in varying gait speed across healthy young adult individuals , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[11]  Richard R Neptune,et al.  Merging of healthy motor modules predicts reduced locomotor performance and muscle coordination complexity post-stroke. , 2010, Journal of neurophysiology.

[12]  Emilio Bizzi,et al.  Combinations of muscle synergies in the construction of a natural motor behavior , 2003, Nature Neuroscience.

[13]  T. Lømo,et al.  Firing patterns of motor units in normal rats , 1985, Nature.

[14]  Ping Zhou,et al.  Multi-scale complexity analysis of muscle coactivation during gait in children with cerebral palsy , 2015, Front. Hum. Neurosci..