Fuzzy granulation is a technique that allows us to represent complex data in a more interpretable and simple way. Granules provide a higher level of abstraction by capturing the essence of the data within specific intervals or ranges. In this work we propose Electrodermal activity (EDA) feature extraction through fuzzy granulation to classify four different scenarios of academic stress. EDA is a physiological signal controlled by the sympathetic nervous system, for this reason it is considered an important component of animical states as stress. According to the research carried out, the detection of stress using EDA is based on the use of morphological attributes or statistical characteristics obtained both in the frequency domain and in time domain. As a time series EDA data often exhibit complex patterns, traditional feature extraction methods may not fully capture these variations, and fuzzy granulation offers an alternative. We have experimented with four different techniques of fuzzy granulation, our results allow us to verify that fuzzy granulation is a useful approach to characterize EDA signals to adequately distinguish the effects of listening to music while performing a stressful task reaching classification accuracy up to 98.9%.