Methods Inf Med 2004; 43(01): 30-35
DOI: 10.1055/s-0038-1633419
Original Article
Schattauer GmbH

Surface EMG Crosstalk Evaluated from Experimental Recordings and Simulated Signals

Reflections on Crosstalk Interpretation, Quantification and Reduction
D. Farina
1   Centro di Bioingegneria, Dip. di Elettronica, Politecnico di Torino, Torino, Italy
,
R. Merletti
1   Centro di Bioingegneria, Dip. di Elettronica, Politecnico di Torino, Torino, Italy
,
B. Indino
1   Centro di Bioingegneria, Dip. di Elettronica, Politecnico di Torino, Torino, Italy
,
T. Graven-Nielsen
2   Center for Sensory-Motor Interaction, Aalborg University, Aalborg, Denmark
› Institutsangaben
Weitere Informationen

Publikationsverlauf

Publikationsdatum:
07. Februar 2018 (online)

Summary

Objectives: Surface EMG crosstalk is the EMG signal detected over a non-active muscle and generated by a nearby muscle. The aim of this study was to analyze the sources of crosstalk signals in surface EMG recordings and to discuss methods proposed in the literature for crosstalk quantification and reduction.

Methods: The study is based on both simulated and experimental signals. The simulated signals are generated by a structure based surface EMG signal model. Signals were recorded with both intramuscular and surface electrodes and single motor unit surface potentials were extracted with the spike triggered averaging approach. Moreover, surface EMG signals were recorded from electrically stimulated muscles.

Results: From the simulation and experimental analysis it was clear that the main determinants of crosstalk are non-propagating signal components, generated by the extinction of the intracellular action potentials at the tendons. Thus, crosstalk signals have a different shape with respect to the signals detected over the active muscle and contain high frequency components.

Conclusions: Since crosstalk has signal components different from those dominant in case of detection from near sources, commonly used methods to quantify and reduce crosstalk, such as the cross-correlation coefficient and high-pass temporal filtering, are not reliable. Selectivity of detection systems must be discussed separately as selectivity with respect to propagating and non-propagating signal components. The knowledge about the origin of crosstalk signal constitutes the basis for crosstalk interpretation, quantification, and reduction.

 
  • References

  • 1 DeLuca CJ, Merletti R. Surface myoelectric signal cross-talk among muscles of the leg. Electroencephalogr Clin Neurophysiol 1988; 69: 568-75.
  • 2 Winter DA, Fuglevand AJ, Archer SE. Crosstalk in surface electromyography: theoretical and practical estimates. J Electromyogr Kinesiol 1993; 4: 15-26.
  • 3 Solomonow M, Baratta R, Bernardi M, Zhou B, Lu Y, Zhu M, Acierno S. Surface and wire EMG crosstalk in neighbouring muscles. J Electromyogr Kinesiol 1994; 4: 131-42.
  • 4 van Vugt JP, van Dijk JG. A convenient method to reduce crosstalk in surface EMG. Clin Neurophysiol 2001; 112: 583-92.
  • 5 Farina D, Merletti R, Indino B, Nazzaro M, Pozzo M. Cross-talk between knee extensor muscles. Experimental and model results. Muscle Nerve 2002; 26: 681-95.
  • 6 Dimitrova NA, Dimitrov GV, Nikitin OA. Neither high-pass filtering nor mathematical differentiation of the EMG signals can considerably reduce cross-talk. J Electromyogr Kinesiol 2002; 12: 235-46.
  • 7 Farina D, Merletti R. A novel approach for precise simulation of the EMG signal detected by surface electrodes. IEEE Trans Biomed Eng 2001; 48: 637-46.
  • 8 Farina D, Arendt-Nielsen L, Merletti R, Indino B, Graven-Nielsen T. Selectivity of spatial filters for surface EMG detection from the tibialis anterior muscle. IEEE Trans Biomed Eng 2003; 50: 354-64.
  • 9 Farina D, Arendt-Nielsen L, Merletti R, Graven-Nielsen T. Assessment of single motor unit conduction velocity during sustained contractions of the tibialis anterior muscle with advanced spike triggered averaging. J Neurosci Meth 2002; 115: 1-12.
  • 10 Merletti R, Farina D, Granata A. Non-invasive assessment of motor unit properties with linear electrode arrays. Electroencephalogr Clin Neurophysiol 1999; 50: 293-300.
  • 11 Merletti R, Farina D, Gazzoni M. The linear electrode array: a useful tool with many applications. J Electromyogr Kinesiol 2003; 13: 37-47.
  • 12 Reucher H, Silny J, Rau G. Spatial filtering of noninvasive multielectrode EMG: Part II – Filter performance in theory and modeling. IEEE Trans Biomed Eng 1987; 34: 106-13.