Document Type : Original Articles


1 Department of Physical Education and Sport Sciences, Islamic Azad University, Bojnourd Branch, Bojnourd, Iran

2 Department of Medical Physics and Radiology, School of Medicine, North Khorasan University of Medical Sciences, Bojnurd, Iran


Background: Strength training has been a common intervention used to improve neuromuscular activity within the synergic and/or agonist-antagonist muscles. This study aimed to evaluate simultaneous electrical activity of quadriceps and hamstring muscles after strength eccentric training versus concentric training.
Methods: This experimental study has a between-group comparison design with a population of 26 males divided into two groups, namely the eccentric training group and the concentric training group. Maximal knee extension force and bipolar surface electromyography (EMG) signals from quadriceps and hamstring muscles were simultaneously recorded pre- and post-concentric and eccentric strength training. After EMG pre-processing for noise reduction, EMG signals were evaluated in two groups by time and frequency analysis. Nine EMG features (six time features and three frequency features) were analyzed in two groups pre- and post-training by statistical analysis.
Results: The results showed that the maximal voluntary isometric contraction (MIVC) of quadriceps muscles was significantly increased in both groups from pre- to post-training (p <0.05). Moreover, eccentric training resulted in greater increases in time features of EMG for quadriceps and hamstring muscles compared to concentric training (p <0.05). All frequency features showed significant changes in pre- and post-tests for the eccentric training group (p <0.05); however, no significant difference was observed in the frequency features in the post-test compared to the pre-test in the concentric training group (p >0.05).
Conclusion: Based on the current results, great changes in time and frequency features of quadriceps and hamstring EMGs were achieved using eccentric training. Thus, eccentric strength training could be more effective in triggering neuromuscular activity within the agonist and antagonist muscles simultaneously.


  1. Thorstensson C, Henriksson M, von Porat A, Sjödahl C, Roos E. The effect of eight weeks of exercise on knee adduction moment in early knee osteoarthritis–a pilot study. Osteoarthritis Cartilage. 2007;15(10):1163-70.
  2. Bates NA, Nesbitt RJ, Shearn JT, Myer GD, Hewett TE. Knee abduction affects greater magnitude of change in ACL and MCL strains than matched internal tibial rotation in vitro. Clin Orthop Relat Res. 2017;475(10):2385-96.
  3. Heijne A, Werner S. Early versus late start of open kinetic chain quadriceps exercises after ACL reconstruction with patellar tendon or hamstring grafts: a prospective randomized outcome study. Knee Surg Sports Traumatol Arthrosc. 2007;15(4):402-14.
  4. Thomas AC, Wojtys EM, Brandon C, Palmieri-Smith RM. Muscle atrophy contributes to quadriceps weakness after anterior cruciate ligament reconstruction. J Sci Med Sport. 2016;19(1):7-11.
  5. Hedayatpour N, Falla D, Arendt-Nielsen L, Farina D. Sensory and electromyographic mapping during delayed-onset muscle soreness. Med Sci Sports Exerc. 2008;40(2):326.
  6. Baz-Valle E, Schoenfeld BJ, Torres-Unda J, Santos-Concejero J, Balsalobre-Fernández C. The effects of exercise variation in muscle thickness, maximal strength and motivation in resistance trained men. PLoS One. 2019;14(12):e0226989.
  7. Zeigham Jahani M, Yaghoubi A, Younessi Heravi MA. Effects of concentric and eccentric strength training on electromyography activity of the knee agonist-antagonist muscles. J Kerman Uni Med Sci. 2021;28(5):478-85.
  8. Reaz MBI, Hussain MS, Mohd-Yasin F. Techniques of EMG signal analysis: detection, processing, classification and applications. Biol Proced Online. 2006;8(1):11-35.
  9. Chowdhury RH, Reaz MB, Ali MABM, Bakar AA, Chellappan K, Chang TG. Surface electromyography signal processing and classification techniques. Sensors. 2013;13(9):12431-66.
  10. Campanini I, Disselhorst-Klug C, Rymer WZ, Merletti R. Surface EMG in clinical assessment and neurorehabilitation: barriers limiting its use. Front Neurol. 2020;11:934.
  11. Veer K, Sharma T. A novel feature extraction for robust EMG pattern recognition. J Med Eng Technol. 2016;40(4):149-54.
  12. Khan AM, Sadiq A, Khawaja SG, Akram MU, Saeed A. Physical action categorization using signal analysis and machine learning. arXiv preprint arXiv:200806971. 2020.
  13. Phinyomark A, Phukpattaranont P, Limsakul C. Feature reduction and selection for EMG signal classification. Expert Systems with Applications. 2012;39(8):7420-31.
  14. Tenore FV, Ramos A, Fahmy A, Acharya S, Etienne-Cummings R, Thakor NV. Decoding of individuated finger movements using surface electromyography. IEEE Trans Biomed Eng. 2008;56(5):1427-34.
  15. Bagheri T, Abedi B, Hedayatpour N. Effects of 12 Weeks Concentric and Eccentric Resistance Training on Neuromuscular Adaptation of Quadriceps Muscle. J Rehabil Sci Res. 2020;7(4):161-6.
  16. Tkach D, Huang H, Kuiken TA. Study of stability of time-domain features for electromyographic pattern recognition. J Neuroeng Rehabil. 2010;7(1):1-13.
  17. Shradhanjali A, Chowdhury S, Kumar N. Power spectral density estimation of EMG signals using parametric and non-parametric approach. Glo Adv Res J Eng Technol Innov. 2013;2(4):111-7.
  18. Zecca M, Micera S, Carrozza M, Dario P. Control of multifunctional prosthetic hands by processing the electromyographic signal. Crit Rev Biomed Eng. 2017;45(1-6).
  19. Han J-S, Song W-K, Kim J-S, Bang W-C, Lee H, Bien Z, editors. New EMG pattern recognition based on soft computing techniques and its application to control of a rehabilitation robotic arm. Proc of 6th Internat Conf Soft Comput (IIZUKA2000); 2000.
  20. Abbaspour S, Lindén M, Gholamhosseini H, Naber A, Ortiz-Catalan M. Evaluation of surface EMG-based recognition algorithms for decoding hand movements. Med Biol Eng Comput. 2020;58(1):83-100.