Semantic feature extraction is a novel field in neurotechnology research and is quickly growing in interest for its many applications, ranging from education to rehabilitation to BCIs. With few studies existing on the subject, a wide range of possibilities open for exploration. While most of the current research landscape explored the implementation of time-domain and frequency-domain features, this study proposes an approach based on entropy measures. exploring their potentiality for differentiation between concepts. This study focused on the analysis of entropy measures computed on EEG signals as, while offering less spatial resolution compared to other brain signal acquisition technologies, it best suits real-life application thanks to its portability, low cost, and current developments for this purpose.