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Techniques of EMG signal analysis: detection, processing, classification and applications


Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.


  1. 1.

    Shahid S. Higher Order Statistics Techniques Applied to EMG Signal Analysis and Characterization. Ph.D. thesis, University of Limerick; Ireland, 2004.

    Google Scholar 

  2. 2.

    Nikias CL, Raghuveer MR. Bispectrum estimation: A digital signal processing framework. IEEE Proceedings on Communications and Radar 1987; 75(7):869–891.

    Google Scholar 

  3. 3.

    Basmajian JV, de Luca CJ. Muscles Alive - The Functions Revealed by Electromyography. The Williams & Wilkins Company; Baltimore, 1985.

    Google Scholar 

  4. 4.

    Kleissen RFM, Buurke JH, Harlaar J, Zilvold G. Electromyography in the biomechanical analysis of human movement and its clinical application. Gait Posture 1998; 8(2):143–158.

    PubMed  Article  Google Scholar 

  5. 5.

    Cram JR, Kasman GS, Holtz J. Introduction to Surface Electromyography. Aspen Publishers Inc.; Gaithersburg, Maryland, 1998.

    Google Scholar 

  6. 6.

    Micera S, Vannozzi G, Sabatini AM, Dario P. Improving detection of muscle activation intervals. IEEE Engineering in Medicine and Biology Magazine 2001; 20(6):38–46.

    PubMed  Article  CAS  Google Scholar 

  7. 7.

    Thexton AJ. A randomization method for discriminating between signal and noise in recordings of rhythmic electromyographic activity. J Neurosci Meth 1996; 66:93–98.

    Article  CAS  Google Scholar 

  8. 8.

    Bornato P, de Alessio T, Knaflitz M. A statistical method for the measurement of the muscle activation intervals from surface myoelectric signal gait. IEEE Trans Biomed Eng 1998; 45:287–299.

    Article  Google Scholar 

  9. 9.

    Winter DA. Pathologic gait diagnosis with computeraveraged electromyographic profiles. Arch Phys Med Rehab 1984; 65:393–398.

    CAS  Google Scholar 

  10. 10.

    Lanyi X, Adler A. An improved method for muscle activation detection during gait. Canadian Conference of Electrical and Computer Engineering 2004; 1:357–360.

    Google Scholar 

  11. 11.

    Merlo A, Farina D. A Fast and Reliable Technique for Muscle Activity Detection from Surface EMG Signals. IEEE Trans Biomed Eng 2003; 50(3): 316–323.

    PubMed  Article  Google Scholar 

  12. 12.

    Fang J, Agarwal GC, Shahani BT. Decomposition of EMG signals by wavelet spectrum matching. Procedures of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 1997; Chicago, IL, USA. pp. 1253–1256.

  13. 13.

    Zennaro D, Welling P, Koch VM, Moschytz GS, Laubli T. A Software Package for the Decomposition of Long-Term Multichannel EMG Signal Using Wavelet Coefficients. IEEE Trans Biomed Eng 2003; 50(1):58–69.

    PubMed  Article  Google Scholar 

  14. 14.

    Yamada R, Ushiba J, Tomita Y, Masakado Y. Decomposition of Electromyographic Signal by Principal Component Analysis of Wavelet Coefficient. IEEE EMBS Asian-Pacific Conference on Biomedical Engineering 2003; Keihanna, Japan. pp. 118–119.

  15. 15.

    Plevin E, Zazula D. Decomposition of surface EMG signals using non-linear LMS optimisation of higherorder cumulants. Proceedings of the 15th IEEE Symposium on Computer-Based Medical System 2002; pp. 149–154.

  16. 16.

    Guglielminotti P, Merletti R. Effect of electrode location on surface myoelectric signal variables: a simulation study. 9th Int. Congress of ISEK 1992; Florence, Italy.

  17. 17.

    Laterza F, Olmo G. Analysis of EMG signals by means of the matched wavelet transform. Electronics Letters 1997; 33(5):357–359.

    Article  Google Scholar 

  18. 18.

    Gabor D. Theory of communication. J Inst Elect Eng 1946; 93:429–457.

    Google Scholar 

  19. 19.

    Ismail AR, Asfour SS. Continuous wavelet transform application to EMG signals during human gait. Thirty-Second Asilomar Conference on Signals, Systems & Computers 1998; 1:325–329.

    Google Scholar 

  20. 20.

    Pattichis CS, Pattichis MS. Time-scale analysis of motor unit action potentials. IEEE Trans Biomed Eng 1999; 46(11):1320–1329.

    PubMed  Article  CAS  Google Scholar 

  21. 21.

    Kumar DK, Pah ND, Bradley A. Wavelet analysis of surface electromyography to determine muscle fatigue. IEEE Trans Neural Syst Rehabil Eng 2003; 11(4):400–406.

    PubMed  Article  Google Scholar 

  22. 22.

    Piper H. Electrophysillogie Muschliche Muskeln. Basel, Switzerland: Verlag von Julius; 1912.

    Google Scholar 

  23. 23.

    Cohen L. Time-frequency analysis. Englewood Cliffs, Prentice-Hall; New Jersey, USA, 1995.

    Google Scholar 

  24. 24.

    Martin W, Flandrin P. Wigner-Ville spectral analysis of nonstationary processes. IEEE Trans Acoust Speech Signal Processing 1985; 33:1461–1470.

    Article  Google Scholar 

  25. 25.

    Amin MG. Time-frequency spectrum analysis and estimation for nonstationary random processes. Time-Frequency Signal Analysis Methods and Applications, Ed: B. Boashash, Longman Chesire 1992; Melbourne, Australia, pp. 208–232.

  26. 26.

    Syeed AM, Jones DL. Optimal kernel for nonstationary spectral estimation. IEEE Trans Signal Processing 1995; 43:478–491.

    Article  Google Scholar 

  27. 27.

    Ricamato AL, Absher RG, Moffroid MT, Tranowski JP. A time-frequency approach to evaluate electromyographic recordings. Proceedings of Fifth Annual IEEE Symposium on Computer-Based Medical Systems 1992; pp. 520–527.

  28. 28.

    Davies MR, Reisman SS. Time frequency analysis of the electromyogram during fatigue. Proceedings of the 20th Annual Northeast Bioengineering Conference 1994; pp. 93–95.

  29. 29.

    Amin M, Cohen L, Williams WJ. Methods and Applications for Time Frequency Analysis. Conference Notes, University of Michigan; 1993.

  30. 30.

    Graupe D, Cline WK. Functional Separation of EMG signals via ARMA identification. IEEE Trans Syst Man Cybern 1975; 5:252–259.

    Google Scholar 

  31. 31.

    Sherif MH. Stochastic Model of Myoelectric Signals for Movement Pattern Recognition in Upper Limb Prostheses. Ph.D. thesis, School of Engineering and Applied Sciences, University of California at Los Angeles, 1980.

  32. 32.

    Doerschuk PC, Gustafson W. Upper Extremity Limb Function Discrimination Using EMG Signal Analysis. IEEE Trans Biomed Eng 1983; 30:18–38.

    PubMed  Article  CAS  Google Scholar 

  33. 33.

    Zhou Y, Chellappa R, Bekey G. Estimation of intramuscular EMG signals from surface EMG signal analysis. IEEE International Conference on Acoustics, Speech, and Signal Processing 1986; 11:1805–1808.

    Google Scholar 

  34. 34.

    Hefftner G, Zucchini W, Jaros G. The electromyogram (EMG) as a control signal for functional neuro-muscular stimulation part 1: Autoregressive modeling as a means of EMG signature discrimination. IEEE Trans Biomed Eng 1988; 35:230–237.

    PubMed  Article  CAS  Google Scholar 

  35. 35.

    Bernatos L, Crago P, Chizeck H. A discrete-time model of electricity stimulated muscle. IEEE Trans Biomed Eng 1986; 33:829–838.

    Article  Google Scholar 

  36. 36.

    Moser A, Graupe D. Identification of nonstationary models with application to myoelectric signals for controlling electrical stimulation of paraplegics. IEEE Trans Acoust Speech Signal Process 1989; 37:713–719.

    Article  Google Scholar 

  37. 37.

    Tohru K. Investigation of Parametric analysis of dynamic EMG signals by a muscle-structured stimulation study. IEEE Trans Biomed Eng 1992; 39(3):280–288.

    Article  Google Scholar 

  38. 38.

    del Boca A, Park DC. Myoelectric signal recognition using fuzzy clustering and artificial neural networks in real time. IEEE International Conference on Neural Networks and IEEE World Congress on Computational Intelligence 1994; 5:3098–3103.

    Google Scholar 

  39. 39.

    Cheron G, Draye J-P, Bourgeios M. A Dynamic Neural Network Identification of electromyography and trajectory relationship during complex movements. IEEE Trans Biomed Eng 1996; 43(5):552–558.

    PubMed  Article  CAS  Google Scholar 

  40. 40.

    Chan FHY, Yang Y-S, Lam FK, Zhang Y-T, Parker PA. Fuzzy EMG classification for prosthesis control. IEEE Transactions Rehabilitation Engineering 2000; 8(3):305–311.

    Article  CAS  Google Scholar 

  41. 41.

    Belouchrani A, Abed-Meraim K, Amin MG, Zoubir A. Joint-antidiagonalization for blind source separation. Procedures in ICASSP 2001; pp. 2789–2792.

  42. 42.

    Farina D, Fevotte C, Doncarli C, Merletti R. Blind separation of linear instantaneous mixtures of nonstationary surface myoelectric signals. IEEE Trans Biomed Eng 2004; 51(9):1555–1567.

    PubMed  Article  Google Scholar 

  43. 43.

    Kanosue K, Yoshida M, Akazawa K, Fujii K. The number of active motor units and their firing rates in voluntary contraction of human brachialiis muscle. Japan J Physiol 1979; 29(4):427–443.

    CAS  Google Scholar 

  44. 44.

    Giannakis GB, Tsatsanis MK. HOS or SOS for parametric modeling? Procedures in IEEE Int Conf Acoustics Speech Signal Process 1991; 5:3097–3100.

    Google Scholar 

  45. 45.

    Yana K, Mizuta H, Kajiyama R. Surface electromyogram recruitment analysis using higher order spectrum. IEEE 17th Annual Conference on Engineering in Medicine and Biology Society 1995; 2:1345–1346.

    Google Scholar 

  46. 46.

    Nikias CL, Petropulu AP. Higher-Spectral Analysis: A Nonlinear Signal Processing Framework. Prentice Hall; New Jersey, 1993.

    Google Scholar 

  47. 47.

    Nikias CL, Mendel JM. Signal Processing with Higher-Order Spectra. IEEE Signal Processing Magazine 1993; 10:10–37.

    Article  Google Scholar 

  48. 48.

    Kaplanis PA, Pattichis CS, Hadjileontiadis LJ, Panas SM. Bispectral analysis of surface EMG. 10th Mediterranean Electrotechnical Conference 2000; 2:770–773.

    Google Scholar 

  49. 49.

    Rosenfalck P. Intra- and extracellular potential fields of active nerve and muscle fibers. Acta Physiol Scand 1969; 321(1):1–169.

    CAS  Google Scholar 

  50. 50.

    Nandedkar SD, Stålberg E. Simulation of single fiber action potentials. Med Biol Eng Comput 1983; 21:158–165.

    PubMed  Article  CAS  Google Scholar 

  51. 51.

    Nandedkar SD, Barkhaus PE. Phase interaction in the compound muscle action potential: application to motor unit estimates. IEEE Colloquium on Neurological Signal Processing 1992; pp. 4/1–4/5.

  52. 52.

    Englehart KB, Parker PA. Single motor unit myoelectric signal analysis with nonstationary data. IEEE Trans Biomed Eng 1994; 42(2):168–180.

    Article  Google Scholar 

  53. 53.

    Zhang YT, Herzog W, Liu MM. A mathematical model of myoelectric signals obtained during locomotion. IEEE 17th Annual Conference on Engineering in Medicine and Biology Society 1995, 2:1403–1404.

    Google Scholar 

  54. 54.

    Karlsson S, Nystrom L. Real-time system for EMG signal analysis of static and dynamic contractions. IEEE 17th Annual Conference on Engineering in Medicine and Biology Society 1995; 2:1347–1348.

    Google Scholar 

  55. 55.

    Duchene J, Hogrel J. A Model of EMG Generation. IEEE Transactions on Biomedical Engineering 2000; 47(2):192–201.

    PubMed  Article  CAS  Google Scholar 

  56. 56.

    de Lorente NR. Analysis of the distribution of action currents of nerve in volume conductors. Stud Rockfeller Inst Med Res 1947; 132:384–477.

    Google Scholar 

  57. 57.

    Hamilton-Wright A, Stashuk DW. Physiologically based simulation of clinical EMG signals. IEEE Transactions on Biomedical Engineering 2005; 52(2):171–183.

    PubMed  Article  Google Scholar 

  58. 58.

    Wellig P, Moschytz GS. Analysis of wavelet features for myoelectric signal classification. IEEE International Conference on Electronics, Circuits and Systems 1998; 3:109–112.

    Google Scholar 

  59. 59.

    Boualem R, Peter O. A Methodology for Detection and Classification of Some Underwater Acoustic Signals Using Time-Frequency Analysis Techniques. IEEE Trans Acoust Speech Signal Processing 1990; 38(11):1829–1841.

    Article  Google Scholar 

  60. 60.

    Zhang LQ, Shiavi R, Hunt MA, Chen J. Clustering analysis and pattern discrimination of EMG linear envelopes. IEEE Trans Biomed Eng 1991; 38(8):777–784.

    PubMed  Article  CAS  Google Scholar 

  61. 61.

    Christodoulou CI, Pattichis CS. A new technique for the classification and decomposition of EMG signals. Proceedings in IEEE International Conference on Neural Networks 1995; 5:2303–2308.

    Article  Google Scholar 

  62. 62.

    McComas AJ, Fawcett PR, Campbell MJ, Sica RE. Electrophysiological estimation of the number of motor units within a human muscle. J Neurol Neurosurg Psychiatry 1971; 34(2):121–131.

    PubMed  Article  CAS  Google Scholar 

  63. 63.

    Stashuk DW, Kassam A, Doherty TJ, Brown WF. Motor Unit Estimates Based on the Automated Analysis of F-Waves. Proceedings of the Annual International Conference on Engineering in Medicine and Biology Society 1992; 14:1452–1453.

    Article  Google Scholar 

  64. 64.

    Zhengquan X, Shaojun X. Estimation of motor unit firing statistics from surface EMG. Proceedings of the 20th Annual International Conference on Engineering in Medicine and Biology Society 1998; 5:2639–2642.

    Google Scholar 

  65. 65.

    Ping Z, Rymer WZ. Motor unit action potential number estimation in the surface electromyogram: wavelet matching method and its performance boundary. Proceedings of the 1st International IEEE EMBS Conference on Neural Engineering 2003; pp. 336–339.

  66. 66.

    Major LA, Jones KE. Simulations of motor unit number estimation techniques. Journal of Neural Engineering 2005; 2(2):17–34.

    PubMed  Article  Google Scholar 

  67. 67.

    Graupe D, Magnussen J, Beex A. A microprocessor system for multifunctional control of upper-limb prostheses via myoelectric signal identification. IEEE Transactions on Automatic Control 1978; 23(4):538–544.

    Article  Google Scholar 

  68. 68.

    Yen C-J, Chung W-Y, Lin K-P, Tsai C-L, Lee S-H, Chen T-S. Analog integrated circuit design for the wireless bio-signal transmission system. The First IEEE Asia Pacific Conference on ASICs 1999; pp. 345–346.

  69. 69.

    Kajitani I, Murakawa M, Nishikawa D, Yokoi H, Kajihara N, Iwata M, Keymeulen D, Sakanashi H, Higuchi T. An evolvable hardware chip for prosthetic hand controller. Proceedings of the Seventh International Conference on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems 1999; pp. 179–186.

  70. 70.

    Torresen J. Two-Step Incremental Evolution of a Prosthetic Hand Controller Based on Digital Logic Gates. ICES 2001; pp. 1–13.

  71. 71.

    Peasgood W, Whitlock T, Bateman A. EMGcontrolled closed loop electrical stimulation using a digital signal processor. Electronics Letters 2000; 36(22):1832–1833.

    Article  Google Scholar 

  72. 72.

    Yeom HJ, Park YC, Yoon YR, Shin TM, Yoon HR. An adaptive M-wave canceller for the EMG controlled functional electrical stimulator and its FPGA implementation. Conference Proceedings in 26th Annual International Conference of Engineering in Medicine and Biology Society 2004; 6:4122–4125.

    CAS  Google Scholar 

  73. 73.

    Ferguson S, Dunlop G. Grasp Recognition From Myoelectric Signals. Procedures Australasian Conference Robotics and Automation 2002; pp. 78–83.

  74. 74.

    Stanford V. Biosignals offer potential for direct interfaces and health monitoring. Pervasive Computing, IEEE 2004; 3(1):99–103.

    Article  Google Scholar 

  75. 75.

    Park DG, Kim HC. Muscleman: Wireless input device for a fighting action game based on the EMG signal and acceleration of the human forearm. [ scleman_paper.pdf].

  76. 76.

    Wheeler KR, Jorgensen CC. Gestures as input: neuroelectric joysticks and keyboards. Pervasive Computing, IEEE 2003; 2(2):56–61.

    Article  Google Scholar 

  77. 77.

    Manabe H, Hiraiwa A, Sugimura T. Unvoiced Speech Recognition using EMG-Mime Speech Recognition. Conference on Human Factors in Computing Systems 2003; pp. 794–795.

  78. 78.

    de Luca CJ. The use of surface electromyography in biomechanics. J Appl Biomech 1997; 13:135–163.

    Google Scholar 

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Correspondence to M. B. I. Reaz.

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Reaz, M.B.I., Hussain, M.S. & Mohd-Yasin, F. Techniques of EMG signal analysis: detection, processing, classification and applications. Biol. Proced. Online 8, 11–35 (2006).

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Indexing terms

  • Electromyography
  • Fourier Analysis
  • Muscles
  • Nervous System