Skip to main content
Log in

Comparison of spatial filter selectivity in surface myoelectric signal detection: Influence of the volume conductor model

  • Published:
Medical and Biological Engineering and Computing Aims and scope Submit manuscript

Abstract

Spatial filters are used for increasing selectivity in surface EMG signal detection. The study investigated the importance of the description of the volume conductor to the inference of conclusions on comparing filter selectivity, from simulation analyses. A cylindrical multi-layer description of the volume conductor was used for the simulation analysis. Different anatomies were analysed with this model, and results on filter selectivity were compared. The longitudinal single (LSD), double (LDD) and normal double differential (Laplacian, NDD) filters were investigated. Largely different conclusions could be drawn when comparing filter selectivity resulting from simulations with different volume conductor models. A filter that performed best with a particular anatomy could be the poorest with another anatomy. With a bone-muscle model and superficial fibres, the ratio between peak-to-peak values of the propagating and non-propagating signal components was approximately 220% for LDD and LSD and lower than for NDD (approximately 290%). With a bone-muscle-fat-skin model, LSD performed significantly worse (150%) than both LDD and NDD, which showed similar performances (approximately 300%). Similarly, if the lateral distance of the recording was increased by 10°, the signal amplitude was reduced to 2% with LSD and LDD and to 4% with NDD. With another anatomy, LSD and LDD reduced signal amplitude to 20–25%, and NDD reduced it to 4%. Similar considerations could be drawn for other selectivity indexes. Thus, modelling should be used carefully to infer conclusions on spatial selectivity and to indicate particular choices of spatial filters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Blok, J.H., Stegeman, D.F., andvan Oosterom A. (2002): ‘Three-layer volume conductor model and software package for applications in surface electromyography’,Ann. Biomed. Eng.,30, pp. 566–577

    Article  Google Scholar 

  • Dimitrov, G.V., andDimitrova, N.A. (1998): ‘Precise and fast calculation of the motor unit potentials detected by a point and rectangular plate electrode’,Med. Eng. Phys.,20, pp. 374–381

    Google Scholar 

  • Dimitrov, G.V., Disselhorst-Klug, C., Dimitrova, N.A., Schulte, E., andRau, G. (2003): ‘Simulation analysis of the ability of different types of multi-electrodes to increase selectivity of detection and to reduce cross-talk’,J. Electromyogr. Kinesiol.,13, pp. 125–138

    Article  Google Scholar 

  • Dimitrova, N.A., Dimitrov, G.V., andNikitin, O.A. (2002): ‘Neither high-pass filtering nor mathematical differentiation of the EMG signals can considerably reduce cross-talk’,J. Electromyogr. Kinesiol.,12, pp. 235–246

    Google Scholar 

  • Disselhorst-Klug, C., Silny, J., andRau, G. (1997): ‘Improvement of spatial resolution in surface-EMG: a theoretical and experimental comparison of different spatial filters’,IEEE Trans. Biomed. Eng.,44, pp. 567–574

    Article  Google Scholar 

  • Disselhorst-Klug, C., Blanc, Y., andRau, G. (1999): ‘Examination of the reduction of crosstalk between the leg muscles by spatial filtering techniques’ ‘Results of small SENIAM projects’ chap. 1, pp. 38–40

  • Farina, D., andMerletti, R. (2001): ‘A novel approach for precise simulation of the EMG signal detected by surface electrodes’,IEEE Trans. Biomed. Eng.,48, pp. 637–646

    Google Scholar 

  • Farina, D., andCescon, C. (2001): ‘Concentric ring electrode systems for non-invasive detection of single motor unit activity’,IEEE Trans. Biomed. Eng.,48, pp. 1326–1334

    Google Scholar 

  • Farina, D., Cescon, C., andMerletti, R. (2002a): ‘Influence of anatomical, physical and detection-system parameters on surface EMG’,Biol. Cybern.,86, pp. 445–456

    Article  Google Scholar 

  • Farina, D., Merletti, R., Indino, B., Nazzaro, M., andPozzo, M. (2002b): ‘Cross-talk between knee extensor muscles. Experimental and modelling results’,Muscle & Nerve,26, pp. 681–695

    Article  Google Scholar 

  • Farina, D., andMerletti, R. (2003): ‘A novel approach for estimating muscle fiber conduction velocity by spatial and temporal filtering of surface EMG signals’,IEEE Trans. Biomed. Eng.,50, pp. 1340–1351

    Google Scholar 

  • Farina, D., Arendt-Nielsen, L., Merletti, R., Indino, B., andGraven-Nielsen, T. (2003a): ‘Selectivity of spatial filters for surface EMG detection from the tibialis anterior muscle’,IEEE Trans. Biomed. Eng.,50, pp. 354–364

    Google Scholar 

  • Farina, D., Schulte, E., Merletti, R., Rau, G., andDisselhorst-Klug, C. (2003b): ‘Single motor unit analysis from spatially filtered surface EMG signals—Part I: spatial selectivity’,Med. Biol. Eng. Comput.,41, pp. 330–337

    Article  Google Scholar 

  • Farina, D., Mesin, L., Martina, S., andMerletti, R. (2003c): ‘A new surface EMG generation model with multi-layer cylindrical description of the volume conductor’,IEEE Trans. Biomed. Eng., (in press)

  • Gootzen, T.H., Stegeman, D.F., andVan Oosterom, A. (1991): ‘Finite limb dimensions and finite muscle length in a model for the generation of electromyographic signals’,Electroenceph. Clin. Neurophysiol.,81, pp. 152–162

    Google Scholar 

  • Gydikov, A., Kossev, A., Trayanova, N., andRadicheva, N. (1986): ‘Selective recording of motor unit potentials’,Electromyogr. Clin. Neurophysiol.,26, pp. 273–281

    Google Scholar 

  • Hogrel, J.Y., andDuchêne, J. (1999): ‘A sEMG-based system for clinical applications using laplacian electrodes’. Proc. 4th General SENIAM Workshop, The Netherlands, pp. 172–177

  • Huppertz, H.J., Disselhorst-Klug, C., Silny, J., Rau, G., andHeimann, G. (1997): ‘Diagnostic yield of noninvasive high-spatial-resolution-EMG in neuromuscular disease’,Muscle & Nerve,20, pp. 1360–1370

    Article  Google Scholar 

  • Lindstrom, L., andMagnusson, R. (1977): ‘Interpretation of myoelectric power spectra: a model and its applications’,Proc. IEEE,65, pp. 653–662

    Google Scholar 

  • Ramaekers, V.T., Disselhorst-Klug, C., Schneider, J., Silny, J., Forst, J., Forst, R., Kotlarek, F., andRau, G. (1993): ‘Clinical application of a noninvasive multi-electrode array EMG for the recording of single motor unit activity’,Neuropediatrics,24, pp. 134–138

    Google Scholar 

  • Rau, G., andDisselhorst-Klug, C. (1997): ‘Principles of high-spatial-resolution surface EMG (HSR-EMG): single motor unit detection and application in the diagnosis of neuromuscular disorders’,J. Electromyogr. Kinesiol.,7, pp. 233–239

    Article  Google Scholar 

  • Reucher, H., Rau, G., andSilny, J. (1987a): ‘Spatial filtering of noninvasive multielectrode EMG: Part I-Introduction to measuring technique and applications’,IEEE Trans. Biomed. Eng.,34, pp. 98–105

    Google Scholar 

  • Reucher, H., Silny, J., andRau, G. (1987b) ‘Spatial filtering of noninvasive multielectrode EMG: Part II-Filter performance in theory and modeling’,IEEE Trans. Biomed. Eng.,34, pp. 106–113

    Google Scholar 

  • Roeleveld, K., Blok, J.H., Stegeman, D.F., andvan Oosterom, A. (1997): ‘Volume conduction models for surface EMG; confrontation with measurements’,J Electromyogr. Kinesiol.,7, pp. 221–232

    Article  Google Scholar 

  • Rosenfalck, P. (1969): ‘Intra and extracellular fields of active nerve and muscle fibers. A physico-mathematical analysis of different models’,Acta Physiol. Scand.,321, pp. 1–49

    Google Scholar 

  • Schulte, E., Farina, D., Rau, G., Merletti, R., andDisselhorst-Klug, C. (2003): ‘Single motor unit analysis from spatially filtered surface EMG signals-Part II: conduction velocity estimation’,Med. Biol. Eng. Comput.,41, pp. 338–345

    Article  Google Scholar 

  • Stegeman, D.F., Blok, J.H., Hermens, H.J., andRoeleveld, K. (2000): ‘Surface EMG models: properties and applications’,J. Electromyogr. Kinesiol.,10, pp. 313–326

    Article  Google Scholar 

  • Van Vugt, J.P.P., andVan Dijk J.G. (2001): ‘A convenient method to reduce crosstalk in surface EMG’,Clin. Neurophysiol.,112, pp. 583–592

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to D. Farina.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Farina, D., Mesin, L., Martina, S. et al. Comparison of spatial filter selectivity in surface myoelectric signal detection: Influence of the volume conductor model. Med. Biol. Eng. Comput. 42, 114–120 (2004). https://doi.org/10.1007/BF02351020

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02351020

Keywords

Navigation