Surface Electromyography to Study Muscle Coordination

  • François Hug
  • Kylie Tucker
Reference work entry


Electromyography (EMG) records the electrical activity that is generated as action potentials propagate along the length of muscle fibers. As such surface EMG is the research tool that is used in a vast majority of the works that assess muscle coordination in health and disease. Although surface EMG recordings can provide valuable information regarding the neural activation of a muscle by the nervous system, there are multiple factors that need to be considered to ensure that the interpretation of the data is accurate. In this chapter, we have highlighted crosstalk, signal cancellation, normalization, computation signal, detection of the onset/offset times, and the misinterpretation of EMG to infer torque as six of the most significant factors that need to be considered when recording and then interpreting EMG data. These factors need to be considered before data is collected, to determine if EMG is the right tool and/or which processing methods may best provide insight into the research question.


EMG Motor control Force Torque Force sharing Crosstalk Signal cancellation Normalization Movement Pattern Profile Activation Motor unit Electrodes Contraction 


  1. Arsenault AB, Winter DA, Marteniuk RG, Hayes KC (1986) How many strides are required for the analysis of electromyographic data in gait? Scand J Rehabil Med 18:133–135Google Scholar
  2. Bareket L, Inzelberg L, Rand D, David-Pur M, Rabinovich D, Brandes B, Hanein Y (2016) Temporary-tattoo for long-term high fidelity biopotential recordings. Sci Rep 6:25727. Scholar
  3. Becker R, Awiszus F (2001) Physiological alterations of maximal voluntary quadriceps activation by changes of knee joint angle. Muscle Nerve 24:667–672CrossRefGoogle Scholar
  4. Bernstein N (1967) Coordination and regulation of movements. Oxford, PergamonGoogle Scholar
  5. Bouillard K, Nordez A, Hodges PW, Cornu C, Hug F (2012) Evidence of changes in load sharing during isometric elbow flexion with ramped torque. J Biomech 45:1424–1429. Scholar
  6. Bruce EN, Goldman MD, Mead J (1977) A digital computer technique for analyzing respiratory muscle EMG’s. J Appl Physiol 43:551–556CrossRefGoogle Scholar
  7. Burden A, Bartlett R (1999) Normalisation of EMG amplitude: an evaluation and comparison of old and new methods. Med Eng Phys 21:247–257CrossRefGoogle Scholar
  8. 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(4):515–526CrossRefGoogle Scholar
  9. Chapman AR, Vicenzino B, Blanch P, Knox JJ, Hodges PW (2010) Intramuscular fine-wire electromyography during cycling: repeatability, normalisation and a comparison to surface electromyography. J Electromyogr Kinesiol 20:108–117. Scholar
  10. Chen HY, Chien CC, Wu SK, Liau JJ, Jan MH (2012) Electromechanical delay of the vastus medialis obliquus and vastus lateralis in individuals with patellofemoral pain syndrome. J Orthop Sports Phys Ther 42:791–796. Scholar
  11. Chiti L, Biondi G, Morelot-Panzini C, Raux M, Similowski T, Hug F (2008) Scalene muscle activity during progressive inspiratory loading under pressure support ventilation in normal humans. Respir Physiol Neurobiol 164:441–448. Scholar
  12. Cowan SM, Hodges PW, Bennell KL, Crossley KM (2002) Altered vastii recruitment when people with patellofemoral pain syndrome complete a postural task. Arch Phys Med Rehabil 83:989–995CrossRefGoogle Scholar
  13. Crossley K, Bennell K, Green S, Cowan S, McConnell J (2002) Physical therapy for patellofemoral pain: a randomized, double-blinded, placebo-controlled trial. Am J Sports Med 30:857–865CrossRefGoogle Scholar
  14. Dankaerts W, O’Sullivan PB, Burnett AF, Straker LM, Danneels LA (2004) Reliability of EMG measurements for trunk muscles during maximal and sub-maximal voluntary isometric contractions in healthy controls and CLBP patients. J Electromyogr Kinesiol 14:333–342. Scholar
  15. De Luca CJ, Merletti R (1988) Surface myoelectric signal cross-talk among muscles of the leg. Electroencephalogr Clin Neurophysiol 69:568–575CrossRefGoogle Scholar
  16. De Luca CJ, Gilmore LD, Kuznetsov M, Roy SH (2010) Filtering the surface EMG signal: Movement artifact and baseline noise contamination. J Biomech 43:1573–1579. Scholar
  17. De Troyer A, Kirkwood PA, Wilson TA (2005) Respiratory action of the intercostal muscles. Physiol Rev 85:717–756. Scholar
  18. Dorel S, Guilhem G, Couturier A, Hug F (2012) Adjustment of muscle coordination during an all-out sprint cycling task. Med Sci Sports Exerc 44:2154–2164. Scholar
  19. Dubo HI, Peat M, Winter DA, Quanbury AO, Hobson DA, Steinke T, Reimer G (1976) Electromyographic temporal analysis of gait: normal human locomotion. Arch Phys Med Rehabil 57:415–420Google Scholar
  20. Edwards RG, Lippold OC (1956) The relation between force and integrated electrical activity in fatigued muscle. J Physiol 132:677–681CrossRefGoogle Scholar
  21. Erdemir A, McLean S, Herzog W, van den Bogert AJ (2007) Model-based estimation of muscle forces exerted during movements. Clin Biomech (Bristol, Avon) 22:131–154. Scholar
  22. Farina D, Merletti R, Enoka RM (2004) The extraction of neural strategies from the surface EMG. J Appl Physiol 96:1486–1495. Scholar
  23. Farina D, Merletti R, Enoka RM (2014) The extraction of neural strategies from the surface EMG: an update. J Appl Physiol 117:1215–1230. Scholar
  24. Frigon A, Carroll TJ, Jones KE, Zehr EP, Collins DF (2007) Ankle position and voluntary contraction alter maximal M waves in soleus and tibialis anterior. Muscle Nerve 35:756–766. Scholar
  25. Fuglevand AJ, Zackowski KM, Huey KA, Enoka RM (1993) Impairment of neuromuscular propagation during human fatiguing contractions at submaximal forces. J Physiol 460:549–572CrossRefGoogle Scholar
  26. Gandevia SC (2001) Spinal and supraspinal factors in human muscle fatigue. Physiol Rev 81:1725–1789CrossRefGoogle Scholar
  27. Guidetti L, Rivellini G, Figura F (1996) EMG patterns during running: intra- and inter-individual variability. J Electromyogr Kinesiol 6:37–48CrossRefGoogle Scholar
  28. Hautier CA, Arsac LM, Deghdegh K, Souquet J, Belli A, Lacour JR (2000) Influence of fatigue on EMG/force ratio and cocontraction in cycling. Med Sci Sports Exerc 32:839–843CrossRefGoogle Scholar
  29. Hermens HJ, Freriks B, Disselhorst-Klug C, Rau G (2000) Development of recommendations for SEMG sensors and sensor placement procedures. J Electromyogr Kinesiol 10:361–374CrossRefGoogle Scholar
  30. Hodges PW, Bui BH (1996) A comparison of computer-based methods for the determination of onset of muscle contraction using electromyography. Electroencephalogr Clin Neurophysiol 101:511–519Google Scholar
  31. Hodges PW, Tucker K (2011) Moving differently in pain: a new theory to explain the adaptation to pain. Pain 152:S90–S98 .–3959(10)00647–0 [pii]CrossRefGoogle Scholar
  32. Hodges PW, Coppieters MW, Macdonald D, Cholewicki J (2013) New insight into motor adaptation to pain revealed by a combination of modelling and empirical approaches. Eur J Pain. Scholar
  33. Hudson AL, Taylor JL, Gandevia SC, Butler JE (2009) Coupling between mechanical and neural behaviour in the human first dorsal interosseous muscle. J Physiol 587:917–925. Scholar
  34. Hug F (2011) Can muscle coordination be precisely studied by surface electromyography? J Electromyogr Kinesiol 21:1–12. Scholar
  35. Hug F, Dorel S (2009) Electromyographic analysis of pedaling: a review. J Electromyogr Kinesiol 19:182–198CrossRefGoogle Scholar
  36. Hug F, Drouet JM, Champoux Y, Couturier A, Dorel S (2008) Interindividual variability of electromyographic patterns and pedal force profiles in trained cyclists. Eur J Appl Physiol 104:667–678CrossRefGoogle Scholar
  37. Hug F, Turpin NA, Guevel A, Dorel S (2010) Is interindividual variability of EMG patterns in trained cyclists related to different muscle synergies? J Appl Physiol 108:1727–1736CrossRefGoogle Scholar
  38. Hug F, Lacourpaille L, Nordez A (2011) Electromechanical delay measured during a voluntary contraction should be interpreted with caution. Muscle Nerve 44:838–839. Scholar
  39. Hug F, Turpin NA, Dorel S, Guevel A (2012) Smoothing of electromyographic signals can influence the number of extracted muscle synergies. Clin Neurophysiol 123:1895–1896. Scholar
  40. Hug F, Goupille C, Baum D, Raiteri BJ, Hodges PW, Tucker K (2015a) Nature of the coupling between neural drive and force-generating capacity in the human quadriceps muscle. Proc Biol Sci 282:1819. Scholar
  41. Hug F, Hodges PW, Tucker K (2015b) Muscle force cannot be directly inferred from muscle activation: illustrated by the proposed imbalance of force between the vastus medialis and vastus lateralis in people with patellofemoral pain. J Orthop Sports Phys Ther 45:360–365. Scholar
  42. Jobe FW, Moynes DR, Tibone JE, Perry J (1984) An EMG analysis of the shoulder in pitching. A second report. Am J Sports Med 12:218–220CrossRefGoogle Scholar
  43. Keenan KG, Farina D, Maluf KS, Merletti R, Enoka RM (2005) Influence of amplitude cancellation on the simulated surface electromyogram. J Appl Physiol 98:120–131. Scholar
  44. Kleissen RF (1990) Effects of electromyographic processing methods on computer-averaged surface electromyographic profiles for the gluteus medius muscle. Phys Ther 70:716–722CrossRefGoogle Scholar
  45. Lawrence JH, De Luca CJ (1983) Myoelectric signal versus force relationship in different human muscles. J Appl Physiol Respir Environ Exerc Physiol 54:1653–1659Google Scholar
  46. Leedham JS, Dowling JJ (1995) Force-length, torque-angle and EMG-joint angle relationships of the human in vivo biceps brachii. Eur J Appl Physiol Occup Physiol 70:421–426CrossRefGoogle Scholar
  47. Li X, Aruin A (2005) Muscle activity onset time detection using teager-kaiser energy operator. Conf Proc IEEE Eng Med Biol Soc 7:7549–7552. Scholar
  48. Linstrom L, Magnusson R, Petersen I (1970) Muscular fatigue and action potential conduction velocity changes studied with frequency analysis of EMG signals. Electromyography 4:341–356Google Scholar
  49. Lowery MM, Stoykov NS, Kuiken TA (2003) Independence of myoelectric control signals examined using a surface EMG model. IEEE Trans Biomed Eng 50:789–793CrossRefGoogle Scholar
  50. Merlo A, Farina D, Merletti R (2003) A fast and reliable technique for muscle activity detection from surface EMG signals. IEEE Trans Biomed Eng 50:316–323. Scholar
  51. 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 31:374–383CrossRefGoogle Scholar
  52. Nordez A, Gallot T, Catheline S, Guevel A, Cornu C, Hug F (2009) Electromechanical delay revisited using very high frame rate ultrasound. J Appl Physiol 106:1970–1975. Scholar
  53. Pal S, Draper CE, Fredericson M, Gold GE, Delp SL, Beaupre GS, Besier TF (2011) Patellar maltracking correlates with vastus medialis activation delay in patellofemoral pain patients. Am J Sports Med 39:590–598. Scholar
  54. Raiteri BJ, Cresswell AG, Lichtwark GA (2015) Ultrasound reveals negligible cocontraction during isometric plantar flexion and dorsiflexion despite the presence of antagonist electromyographic activity. J Appl Physiol 118:1193–1199. Scholar
  55. Raiteri BJ, Hug F, Cresswell AG, Lichtwark GA (2016) Quantification of muscle co-contraction using supersonic shear wave imaging. J Biomech 49:493–495. Scholar
  56. Rouffet DM, Hautier CA (2007) EMG normalization to study muscle activation in cycling. J Electromyogr Kinesiol 18(5):866–878CrossRefGoogle Scholar
  57. Ryan MM, Gregor RJ (1992) EMG profiles of lower extremity muscles during cycling at constant workload and cadence. J Electromyogr Kinesiol 2:69–80CrossRefGoogle Scholar
  58. Salomoni S, Tucker K, Hug F, McPhee M, Hodges P (2016) Reduced maximal force during acute anterior knee pain is associated with deficits in voluntary muscle activation. PLoS One 11:e0161487. Scholar
  59. Shiavi R, Champion S, Freeman F, Griffin P (1981) Variability of electromyographic patterns for level-surface walking through a range of self-selected speeds. Bull Prosthet Res 10–35:5–14Google Scholar
  60. Shiavi R, Frigo C, Pedotti A (1998) Electromyographic signals during gait: criteria for envelope filtering and number of strides. Med Biol Eng Comput 36:171–178CrossRefGoogle Scholar
  61. Soderberg GL, Knutson LM (2000) A guide for use and interpretation of kinesiologic electromyographic data. Phys Ther 80:485–498Google Scholar
  62. Staude GH (2001) Precise onset detection of human motor responses using a whitening filter and the log-likelihood-ratio test. IEEE Trans Biomed Eng 48:1292–1305. Scholar
  63. Staude G, Wolf W (1999) Objective motor response onset detection in surface myoelectric signals. Med Eng Phys 21:449–467CrossRefGoogle Scholar
  64. Valero-Cuevas FJ, Cohn BA, Yngvason HF, Lawrence EL (2015) Exploring the high-dimensional structure of muscle redundancy via subject-specific and generic musculoskeletal models. J Biomech 48:2887–2896. Scholar
  65. van Vugt JP, van Dijk JG (2001) A convenient method to reduce crosstalk in surface EMG. Clin Neurophysiol 112:583–592CrossRefGoogle Scholar
  66. Winter EM, Brookes FB (1991) Electromechanical response times and muscle elasticity in men and women. Eur J Appl Physiol Occup Physiol 63:124–128CrossRefGoogle Scholar
  67. Winter DA, Yack HJ (1987) EMG profiles during normal human walking: stride-to-stride and inter-subject variability. Electroencephalogr Clin Neurophysiol 67:402–411CrossRefGoogle Scholar
  68. Winter DA, Fuglevand AJ, Archer SE (1994) Crosstalk in surface electromyography: theoretical and practical estimates. J Electromyogr Kinesiol 4:15–26CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratory “Movement, Interaction, Performance” (EA4334)University of NantesNantesFrance
  2. 2.NHMRC Centre of Clinical Research Excellence in Spinal Pain, Injury and Health, School of Health and Rehabilitation SciencesThe University of QueenslandBrisbaneAustralia
  3. 3.School of Biomedical SciencesThe University of QueenslandBrisbaneAustralia

Section editors and affiliations

  • William Scott Selbie
    • 1
  1. 1.Has-Motion Inc.KingstonCanada

Personalised recommendations