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Crosstalk in surface electromyogram: literature review and some insights

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Abstract

Surface electromyogram (EMG) has a relatively large pick-up volume, reflecting the activity of muscle tissue placed quite far from the electrodes. This could be beneficial when the global muscle activity is of interest, but it is a limitation when selective information is needed. The EMG from muscles that are neighbors of the one of interest is called crosstalk. Its interpretation, identification, quantification and removal have been the objectives of many works in the literature. However, it is still considered as an open problem, with effects that are difficult to predict. In this paper, the problem of crosstalk is discussed and the main literature is reviewed. Finally, a few recent techniques are introduced that are potentially relevant to quantify or reduce it.

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References

  1. Hug F (2011) Can muscle coordination be precisely studied by surface electromyography? J Electromyogr Kinesiol 21:1–12

    PubMed  Google Scholar 

  2. Jiang N, Englehart KB, Parker PA (2009) Extracting simultaneous and proportional neural control information for multiple-DOF prostheses from the surface electromyographic signal. IEEE Trans Biomed Eng 56:1070–80

    PubMed  Google Scholar 

  3. Mitchell Barr K, Miller AL, Chapin KB (2010) Surface electromyography does not accurately reflect rectus femoris activity during gait: Impact of speed and crouch on vasti-to-rectus crosstalk. Gait & Posture 32:363–8

    Google Scholar 

  4. Kong Yong-Ku, Hallbeck MS, Jung Myung-Chul (2010) Crosstalk effect on surface electromyogram of the forearm flexors during a static grip task. J Electromyogr Kinesiol 20:1223–9

    PubMed  Google Scholar 

  5. Mezzarane RA, Kohn AF (2009) A method to estimate EMG crosstalk between two muscles based on the silent period following an H-reflex. Med Eng Phys 31:1331–6

    PubMed  Google Scholar 

  6. Lowery MM, Stoykov NS, Taflove A, Kuiken TA (2002) A multiplelayer finiteelement model of the surface EMG signal. IEEE Trans Biomed Eng 49:446–54

    PubMed  Google Scholar 

  7. Lowery MM, Stoykov NS, Dewald JP, Kuiken TA (2004) Volume conduction in an anatomically based surface EMG model. IEEE Trans Biomed Eng 51:2138–47

    PubMed  Google Scholar 

  8. Mesin L (2008) Simulation of surface EMG signals for a multilayer volume conductor with a superficial bone or blood vessel. IEEE Trans Biomed Eng 55:1647–57

    PubMed  Google Scholar 

  9. Mesin L, Smith S, Hugo S, Viljoen S, Hanekom T (2009) Effect of spatial filtering on crosstalk reduction in surface EMG recordings. Med Eng Phys 31:374–83

    PubMed  Google Scholar 

  10. Stoykov NS, Lowery MM, Kuiken TA (2005) A finite-element analysis of the effect of muscle insulation and shielding on the surface EMG signal. IEEE Trans Biomed Eng 52:117–21

    PubMed  Google Scholar 

  11. Farina D, Merletti R, Indino B, Nazzaro M, Pozzo M (2002) Surface EMG crosstalk between knee extensor muscles: experimental and model results. Muscle Nerve 26:681–95

    PubMed  Google Scholar 

  12. Farina D, Arendt-Nielsen L, Merletti R, Indino B, Graven-Nielsen T (2003) Selectivity of spatial filters for surface EMG detection from the tibialis anterior muscle. IEEE Trans Biomed Eng 50:354–64

    PubMed  Google Scholar 

  13. Farina D, Févotte C, Doncarli C, Merletti R (2004) Blind separation of linear instantaneous mixtures of nonstationary surface myoelectric signals. IEEE Trans Biomed Eng 51:1555–67

    PubMed  Google Scholar 

  14. Merletti R, De Luca CJ, Sathyan D (1994) Electrically evoked myoelectric signals in back muscles: effect of side dominance. J Appl Physiol 77:2104–14

    CAS  PubMed  Google Scholar 

  15. Talib I, Sundaraj K, Lam CK, Hussain J, Ali MA (2018) A review on crosstalk in myographic signals. Eur J Appl Physiol (Review)

  16. De Luca CJ, Merletti R (1988) Surface myoelectric signal crosstalk among muscles of the leg. Electroencephalogr Clin Neurophysiol 69:568–75

    PubMed  Google Scholar 

  17. Vieira TM, Botter A, Muceli S, Farina D (2017) Specificity of surface EMG recordings for gastrocnemius during upright standing. Sci Rep 7(1):13300

    PubMed  PubMed Central  Google Scholar 

  18. Mogk JPM, Keir PJ (2003) Crosstalk in surface electromyography of the proximal forearm during grip** tasks. J Electromyogr Kinesiol 13:63–71

    PubMed  Google Scholar 

  19. Sinderby C, Friberg S, Comtois N (1985) Grassino A (1996) Chest wall muscle cross talk in canine costal diaphragm electromyogram. J Appl Physiol 81(5):2312–27

    Google Scholar 

  20. Frahm KS, Jensen MB, Farina D, Andersen OK (2012) Surface EMG crosstalk during phasic involuntary muscle activation in the nociceptive withdrawal reflex. Muscle Nerve 46(2):228–36

    PubMed  Google Scholar 

  21. Solomonow M, Baratta R, Bernardi M, Zhou B, Lu Y, Zhu M, Acierno S (1994) Surface and wire EMG crosstalk in neighbouring muscles. J Electromyogr Kinesiol 4:131–42

    CAS  PubMed  Google Scholar 

  22. Campanini I, Merlo A, Degola P, Merletti R, Vezzosi G, Farina D (2007) Effect of electrode location on EMG signal envelope in leg muscles during gait. J Electromyogr Kinesiol 17:515–26

    CAS  PubMed  Google Scholar 

  23. Dimitrova NA, Dimitrov GV, Nikitin OA (2002) Neither high-pass filtering nor mathematical differentiation of the EMG signals can considerably reduce cross-talk. J Electromyogr Kinesiol 12:235–46

    CAS  PubMed  Google Scholar 

  24. Lowery MM, Stoykov NS, Kuiken TA (2003) A simulation study to examine the use of cross-correlation as an estimate of surface EMG cross talk. J Appl Physiol 94:1324–34

    PubMed  Google Scholar 

  25. De Luca CJ, Kuznetsov M, Donald Gilmore L, Roy SH (2012) Interelectrode spacing of surface EMG sensors: reduction of crosstalk contamination during voluntary contractions. J Biomechanics 45:555–61

    Google Scholar 

  26. Mesin L (2018) Optimal spatio-temporal filter for the reduction of cross-talk in surface electromyogram. J Neural Eng 15(1):016013

    PubMed  Google Scholar 

  27. van Oosterom A (1998) Principles in inverse electrophysiological modelling, 6th Deliverable of the SENIAM project, pp 37–44

  28. Dimitrov GV, Disselhorst-Klug C, Dimitrova NA, Schulte E, Rau 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 13:125–38

    PubMed  Google Scholar 

  29. Disselhorst-Klug C, Silny J, Rau G (1997) Improvement of spatial resolution in surface-EMG: a theoretical and experimental comparison of different spatial filters. IEEE Trans Biomed Eng 44:567–74

    CAS  PubMed  Google Scholar 

  30. Duchene J, Hogrel JY (2000) A model of EMG generation. IEEE Trans Biomed Eng 47:192–201

    CAS  PubMed  Google Scholar 

  31. Mesin L, Cescon C, Gazzoni M, Merletti R, Rainoldi A (2009) A bi-dimensional index for the selective assessment of myoelectric manifestations of peripheral and central muscle fatigue. J Electromyogr Kinesiol 19:851–63

    PubMed  Google Scholar 

  32. Dimitrova NA, Dimitrov GV (2003) Interpretation of EMG changes with fatigue: facts, pitfalls, and fallacies. J Electromyogr Kinesiol 13:13–36

    CAS  PubMed  Google Scholar 

  33. Roeleveld K, Blok JH, Stegeman DF, van Oosterom A (1997) Volume conduction models for surface EMG confrontation with measurements. J Electromyogr Kinesiol 7:221–32

    CAS  PubMed  Google Scholar 

  34. Stegeman DF, Blok JH, Hermens HJ, Roeleveld K (2000) Surface EMG models: properties and applications. J Electromyogr Kinesiol 10:313–26

    CAS  PubMed  Google Scholar 

  35. Shwedyk E, Balasubramanian R, Scott R (1977) A non-stationary model of the electromyogram. IEEE Trans Biomed Eng 24:417–24

    CAS  PubMed  Google Scholar 

  36. Mesin L (2017) Mathematical models for biomedicine, ISBN: 9788892332980

  37. Mesin L (2019) Neuromuscular system engineering, ISBN: 9788892361881

  38. Mesin L, Merletti R (2008) Distribution of electrical stimulation current in a planar multilayer anisotropic tissue. IEEE Trans Biomed Eng 55(2 Pt 1):660–70

    PubMed  Google Scholar 

  39. Fuglevand AJ, Winter DA, Patla AE (1993) Models of recruitment and rate coding organization in motor-unit pools. J Neurophysiol 70:2470–88

    CAS  PubMed  Google Scholar 

  40. Farina D, Merletti R (2001) A novel approach for precise simulation of the EMG signal detected by surface electrodes. IEEE Trans Biomed Eng 48:637–46

    CAS  PubMed  Google Scholar 

  41. Mesin L (2006) Simulation of surface EMG signals for a multi-layer volume conductor with triangular model of the muscle tissue. IEEE Trans Biomed Eng 53:2177–84

    PubMed  Google Scholar 

  42. Farina D, Mesin L, Martina S (2004) Advances in surface EMG signal simulation with analytical and numerical descriptions of the volume conductor. Med Biol Eng Comput 42:467–76

    CAS  PubMed  Google Scholar 

  43. Mesin L, Joubert M, Hanekom T, Merletti R, Farina D (2005) A finite element model for describing the effect of muscle shortening on surface EMG. IEEE Trans Biomed Eng 53:593–600

    Google Scholar 

  44. Mesin L (2013) Volume conductor models in surface electromyography: computational techniques. Comput Biol Med 43:942–52

    PubMed  Google Scholar 

  45. Mesin L (2013) Volume conductor models in surface electromyography: applications to signal interpretation and algorithm test. Comput Biol Med 43:953–61

    PubMed  Google Scholar 

  46. Mesin L (2017) Introduction to biomedical signal processing, ISBN: 9788892322721

  47. Farina D, Mesin L, Martina S, Merletti R (2004) Comparison of spatial filter selectivity in surface myoelectric signal detection - Influence of the volume conductor model. Med Biol Eng Comput 42:114–120

    CAS  PubMed  Google Scholar 

  48. Mesin L, Kandoor AKR, Merletti R (2008) Separation of propagating and non-propagating components in surface EMG. Biomed Signal Process Control 3:126–37

    Google Scholar 

  49. Mesin L (2019) Separation of interference surface electromyogram into propagating and non-propagating components. Biomed Signal Process Control 52:238–47

  50. Gallina A, Botter A (2013) Spatial localization of electromyographic amplitude distributions associated to the activation of dorsal forearm muscles. Front Physiol 4:367

    PubMed  PubMed Central  Google Scholar 

  51. Mesin L, Merletti R, Vieira TMM (2011) Insights gained into the interpretation of surface electromyograms from the gastrocnemius muscles: a simulation study. J Biomech 44:1096–103

    PubMed  Google Scholar 

  52. Vieira TMM, Loram ID, Muceli S, Merletti R, Farina D (2011) Postural activation of the human medial gastrocnemius muscle: are the muscle units spatially localised? J Physiol 589:431–43

    CAS  PubMed  Google Scholar 

  53. Hodges PW, Gandevia SC (2000) Pitfalls of intramuscular electromyographic recordings from the human costal diaphragm. Clin Neurophysiol 111(8):1420–4

    CAS  PubMed  Google Scholar 

  54. Kilner JM, Baker SN, Lemon RN (2002) A novel algorithm to remove electrical cross-talk between surface EMG recordings and its application to the measurement of short-term synchronisation in humans. J Physiol 538(Pt 3):919–30

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Mesin L, Holobar A, Merletti R (2011) Blind source separation: application to biomedical signals. In: Cerutti S, Marchesi C (eds) Advanced methods of biomedical signal processing. IEEE Press series in biomedical engineering. Wiley, pp 379–409

  56. Holobar A, Zazula D (2007) Multichannel blind source separation using convolution kernel compensation. IEEE Trans Signal Process 55:4487–96

    Google Scholar 

  57. Belouchrani A, Abed-Meraim K, Cardoso J-F, Moulines E (1997) A blind source separation technique using second-order statistics. IEEE Trans Signal Process 45(2):434–44

    Google Scholar 

  58. Michel CM, Murray MM, Lantz G, Gonzalez S, Spinelli L, de Peralta RG (2004) EEG source imaging. Clin Neurophysiol 115:2195–222

    PubMed  Google Scholar 

  59. Chauvet E, Fokapu O, Gamet D (2001) Inverse problem in the surface EMG: a feasibility study. In: Proceedings of the 23rd annual EMBS international conference, Istanbul, Turkey, pp 1048–1050

  60. Jesinger RA, Stonick VL (1994) Processing signals from surface electrode arrays for noninvasive 3D map** of muscle activity. In: IEEE DSP workshop proceedings, pp 57–60

  61. LoPresti EF, Jesinger RA, Stonick VL (1995) Identifying significant frequencies in surface EMG signals for localization of muscle activity. In: IEEE EMBS conference proceedings, pp 967–968

  62. Mesin L (2015) Real time identification of active regions in muscles from high density surface electromyogram. Comput Biol Med 56:37–50

    PubMed  Google Scholar 

  63. Roeleveld K, Stegeman DF, Vingerhoets HM, van Oosterom A (1997) The motor unit potential distribution over the skin surface, its use in estimating the motor unit location. Acta Physiol Scand 161:465–72

    CAS  PubMed  Google Scholar 

  64. Stonick JT, Jesinger RA, Stonick VL, Baumann SB (1996) Estimation and localization of multiple dipole sources for noninvasive map** of muscle activity. IEEE Proc Int Conf Acoust Speech Signal Process 5:2912–5

    Google Scholar 

  65. van den Doel K, Ascher UM, Pai DK (2008) Computed myography: three dimensional reconstruction of motor functions from surface EMG data. Inverse Prob 24:065010

    Google Scholar 

  66. van den Doel K, Ascher UM, Pai DK (2011) Source localization in electromyography using the inverse potential problem. Inverse Prob 27:025008

    Google Scholar 

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Correspondence to Luca Mesin.

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Mesin, L. Crosstalk in surface electromyogram: literature review and some insights. Phys Eng Sci Med 43, 481–492 (2020). https://doi.org/10.1007/s13246-020-00868-1

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