IFKB – Spatiotemporal patterns in high-density surface electromyography

IFKB – Spatiotemporal patterns in high-density surface electromyography #SWI2005

Being recorded at the skin, a surface electromyographic signal (sEMG) reflects the
electrical activity of an underlying muscle (or group of muscles). Hence, sEMG offers
a fairly simple, non-invasive way to assess the activation of superficial muscles. The
signal is an integral measure summing action potentials of many motor units, i.e.
groups of muscle fibers that are innervated by a single nerve fiber. The biophysics
involved in the production of the motor unit action potential can be considered well
understood and there is wide accord regarding basic ingredients for a corresponding
Mathematical models are typically designed to support the application of
sEMG in fields like ergonomics, biomechanics, and kinesiology. They primarily serve
for identifying which muscles are involved in certain performances, determining the
strength and timing of muscle activity, or monitoring the muscle’s physiology during
different activities. In this sense models are used to relate physiological and anatomical
parameters to global variables like mean intensities and power spectral distributions,
that is, coarse-grained variables of sEMG that are assumed to reflect a muscle’s
overall state. Recently developed, rather sophisticated techniques, however,
allow for much more fine-grained experimental approaches: high-density sEMG provides
a detailed spatial resolution by using large arrays of electrodes when recording
muscle activity and gives thus the opportunity to analyze the development of the spatially
extended activity of the muscle. The corresponding spatiotemporal patterns display
both traveling solitary waves along the direction of muscle fibers and diffusive
spreading of electric activity either due to cross-talk between neighboring muscle fibers
and/or because of volume conduction (passive conduction through the surrounding


The question arises to what extent traveling waves and diffusion patterns can
be pinpointed given fundamental physiological and anatomical properties. If such a
relation can be deduced, then one can continue asking how measurement conditions
and models should be adapted to maximize the information content of the parameters
in these models.

McGill KC, Surface electromyogram signal modelling. Medical & Biological Engineering & Computing,
42:446, 2004
Stegeman DF, Blok, JH, Hermens HJ, Roeleveld K. Surface EMG models: properties and applications,
Journal of Electromyography and Kinesiology, 10:313, 2000
Zwarts MJ, Stegeman DF, Multichannel surface EMG: basic aspects and clinical utility, Muscle Nerve,
28:1, 2003


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IFKB – Spatiotemporal patterns in high-density surface electromyography