Muscle synergies are considered as a potential strategy to reduce the computational workload undergoing the estimation of muscle activity during different motor tasks. They are usually extracted by means of algebraic factorization algorithms able to capture the greatest communality of a set of electromyographic (EMG) signals. Usually EMG signals are pooled across different sub-movements (e.g., going forward and backward during reaching) in order to increase the complexity of the data set and, consequently, capture the maximum communality. Despite of these, this preliminary study was designed to investigate how the communality of EMG signals can be explained looking at narrow subset of recorded signals. Results corroborate the hypothesis that using a suitable subset of the whole dataset can significantly modify the values of weight coefficients. In this regard, further methodological investigations of algorithms adopted for synergy extraction are still required.