Simulated Surface Electromyographic (SEMG) Signal Generation and Detection Model

[1] Östlund N. Yu J. Roeleveld K. Karlsson JS. Adaptive spatial filtering of multichannel surface electromyogram signals. Med. Biol. Eng. Comput 2004; 42(6): 825–831. Search in Google Scholar

[2] Farina D. Merletti R. A novel approach for precise simulation of the EMG signal detected by surface electrodes. IEEE. Trans. Biomed. Eng 2001; 48 (6): 637–646. Search in Google Scholar

[3] Mesin L. Farina D. Simulation of surface EMG signals generated by muscle tissues with inhomogeneity due to fiber pinnation. IEEE. Trans. Biomed. Eng 2004; 51 (9): 1521–1529. Search in Google Scholar

[4] Mesin L. Simulation of surface EMG signals for a multilayer volume conductor with triangular model of the muscle tissue. IEEE. Trans. Biomed. Eng 2006; 53(11): 2177–2184. Search in Google Scholar

[5] Fuglevand AJ. Winter DA. Patla AE. Models of recruitment and rate coding organization in motor-unit pools. J. Neurophysiol 1993; 70 (6): 2470–2488. Search in Google Scholar

[6] Stashuk DW. Simulation of electromyographic signals. J. Electromyogra. Kinesiol 1993 3 (3): 157–173. Search in Google Scholar

[7] Wang W. Stefano Ade. Allen R. A simulation model of the surface EMG signal for analysis of muscle activity during the gait cycle. Comput. Biol. Med 2006; 36 (6): 601–618. Search in Google Scholar

[8] Keenan KG. Farina D. Meyer FG. Merletti R. Enoka RM. Sensitivity of the cross-correlation between simulated surface EMGs for two muscles to detect motor unit synchronization. J. Appl. Physiol 2007 102 (3): 1193–1201. Search in Google Scholar

[9] Merletti R. Lo Conte L. Avignone E. Guglielminotti P. Modelling of surface myoelectric signals – part I: model implementation. IEEE. Trans. Biomed. Eng 1999; 46 (7): 810–820. Search in Google Scholar

[10] Farina D. Alberto R. Compensation of the effect of sub-cutaneous tissue layers on surface EMG : a simulation study. Med. Eng. Physics 1999; 21 (6–7): 487–497. Search in Google Scholar

[11] Mesin L. Farina F. A model for surface EMG generation in volume conductors with spherical inhomogeneties. IEEE. Trans. Biomed. Eng 2005; 52 (12): 1984–1993. Search in Google Scholar

[12] Mesin L. Farina D. An analytical model for surface EMG generation in volume conductors with smooth conductivity variations. IEEE. Trans. Biomed. Eng 2006; 53 (5): 773–779. Search in Google Scholar

[13] Dimitrov GV. Dimitrova N.A. Precise and fast calculation of the motor unit potentials detected by a point and rectangular plate electrode. Med. Eng. Phys 1998; 20 (5): 374–381. Search in Google Scholar

[14] Farina D. Mesin L. Martina S. Advances in surface EMG signal simulation with analytical and numerical descriptions of the volume conductor. Med. Biol. Eng. Comput 2004; 42 (4): 467–476. Search in Google Scholar

[15] Lowery MM. Stoykov NS. Taflove A. Kuiken TA. A multiple-layer finite-element model of the surface EMG signal, IEEE. Trans. Biomed. Eng 2002; 49 (5): 446–454. Search in Google Scholar

[16] Mesin L. Joubert M. Hanekom T. Merletti R. Farina D. A Finite Element Model for Describing the Effect of Muscle Shortening on Surface EMG. IEEE. Trans. Biomed. Eng 2005; 53 (4): 593–600. Search in Google Scholar

[17] Farina D. Mesin L. Martina S. Merletti R. A surface EMG generation model with multilayer cylindrical description of the volume conductor. IEEE. Trans. Biomed. Eng 2004; 51 (3): 415–426. Search in Google Scholar

[18] Blok JH. Stegeman DF. Van Oosterom A. Three-layer volume conductor model and software package for applications in surface electromyography. Ann. Biomed. Eng 2002; 30 (4): 566–577. Search in Google Scholar

[19] Gootzen TH. Stegeman DF. Van Oosterom A. Finite limb dimensions and finite muscle length in a model for the generation of electromyographic signals, Electroencephalogr. Clin. Neurophysiol 1991; 81 (2): 152–162. Search in Google Scholar

[20] Mesin L. Simulation of surface EMG signals for a multilayer volume conductor with a superficial bone or blood vessel, IEEE. Trans. Biomed. Eng 2008; 55 (6): 1647–1657. Search in Google Scholar

[21] Messaoudi N. Bekka RE. Philippe R. Harba R. Assessment of the non-Gaussianity and non-linearity levels of simulated sEMG signals on stationary segments, J. Electromyogr. Kinesiol 2017; 32 (Feb): 70–82. Search in Google Scholar

[22] Rosenfalck P. Intra and extracellular fields of active nerve and muscle fibres: A physico-mathematical analysis of different models. Acta. Physiol. Scand 1969; 321: 1–168. Search in Google Scholar

[23] Messaoudi N. Bekka RE. Belkacem S. Effects of detection system parameters on cross-correlations between MUAPs generated from parallel and inclined muscle fibers. Polish. J. Med. Phys. Eng 2021; 27 (1): 87–97. Search in Google Scholar

[24] Messaoudi N. Belkacem S. Bekka RE. Ability of spatial filters to distinguish between two MUAPs generated from MUs with different locations, sizes and fibers pennation. J. Instrument, 2023; 18 (3): P03041. Search in Google Scholar

[25] Enoka RM. Motor Unit. Wiley Encyclopedia of Biomedical Engineering, 2006. Search in Google Scholar

[26] Hu X. Rymer WZ. Suresh N. Motor unit firing rate patterns during voluntary muscle force generation: a simulation study. J. Neural. Eng 2014; 11 (2): 026015. Search in Google Scholar

[27] De Luca CJ. Hostage EC. Relationship between firing rate and recruitment threshold of motoneurons in voluntary isometric contractions. J. Neurophysiol 2010; 104 (2) 1034–1046. Search in Google Scholar

[28] Hu X. Rymer WZ. Suresh NL. Motor unit pool organization examined via spike-triggered averaging of the surface Electromyogram. J. Neurophysiol 2013; 110 (5): 1205–1220. Search in Google Scholar

[29] Messaoudi N. Bekka R.E. (2015). From Single Fiber Action Potential to Surface Electromyographic Signal: A Simulation Study. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2015. Lecture Notes in Computer Science, vol 9043. Springer, Cham. https://doi.org/10.1007/978-3-319-16483-0_32. Search in Google Scholar

[30] Messaoudi N. Bekka RE. Belkacem S. Cross-Correlation Coefficient as a Means for Estimating the Effect of MVC level According to the Fibres Inclination. The Fifth International Conference on Electrical Engineering, ICEE2017, Boumerdes, Algeria, Proceedings, IEEE Xplore (2017b). Search in Google Scholar

[31] Messaoudi N. Bekka RE. Belkacem S. Classification of the systems used in surface electromyographic signals detection according to the degree of isotropy. Adv. Biomed. Eng 2018; 7 (1): 107–116. Search in Google Scholar

[32] Belkacem S. Bekka RE. Messaoudi N. Influence of MVC on Temporal and Spectral Features of Simulated Surface Electromyographic Signals. Critical. Reviews. Biomed. Eng 2019; 47 (5): 409–418. Search in Google Scholar

[33] Messaoudi N. Bekka RE. Belkacem S. Influence of Fibers Inclination on the Degree of Gaussianity of Simulated Surface EMG Signals. ICBBT 2020, May 22–24, 2020, Xi’an, China. Search in Google Scholar

[34] Merletti R. Temporiti F. Gatti R. Gupta S. Sandrini G. Serrao M. Translation of surface electromyography to clinical and motor rehabilitation applications: The need for new clinical figures. Translatio. Neurosci 2023; 14 (1): 1 20220279. Search in Google Scholar

[35] Campanini I. Merlo A. Disselhorst-Klug C. Mesin L. Muceli S. Merletti R. Fundamental Concepts of Bipolar and High-Density Surface EMG Understanding and Teaching for Clinical, Occupational, and Sport Applications: Origin, Detection, and Main Errors. Sensors 2022; 22 (11) 4150. Search in Google Scholar