Surface electromyogram (sEMG) represents a ubiquitous tool for estimating muscle action potentials. For instance, during pre-determined voluntary movements, SEMG analysis can permit knowledge of the muscle activation sequence either in the lower or upper extremities. In general, sEMG sensing is feasible to evaluate muscle activity patterns for function, control and learning. For achieving this, multiple electrical sensors are essential for extracting intrinsic physiological and contextual information from the corresponding sEMG signals. The reason, why more than just one sEMG signal capture has to be used, is as follows: Due to signal propagation inside the human body in terms of an electrical conductor, there cannot be a one-to-one mapping of activities between muscle fibre groups and corresponding SEMG sensing electrodes. Each of such electrodes rather records a composition of many, and widely active-independent signals, and such kind of raw signal capture cannot be efficiently used for pattern matching due to its linear dependency. On the other hand, ICA provides the perfect answer of de-convolving a set of skin surface recordings into a vector (set) of independent muscle actions. Hence, there is need for a method that indicates the quality of the sensor set in sEMG recording. The purpose of this paper is to describe the use of source separation for sEMG based on Independent Component Analysis (ICA). We demonstrate how this can be used in practical sEMG experiments, when the number of recording channels for electrical muscle activities is varied. These results are funded on a wide set of experiments.