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Simulation models are unavoidable in experimental research when the point is to develop new processing algorithms to be applied on real signals in order to extract specific parameter values. Such algorithms have generally to be optimized by comparing true parameter values to those deduced from the algorithm. Only a simulation model can allow the user to access and control the actual process parameter values. This constraint is especially true when dealing with biomedical signals like surface electromyogram (SEMG). This work is an attempt to produce an efficient SEMG simulation model as a help for assessing algorithms related to SEMG features description. It takes into account the most important parameters which could influence these characteristics. This model includes all transformations from intracellular potential to surface recordings as well as a fast implementation of the extracellular potential computation. In addition, this model allows multiple graphically-programmable electrode-set configurations and SEMG simulation in both voluntary and elicited contractions.