Emotion recognition based on electroencephalography (EEG) is a field that has attracted significant attention in recent years since this technique can directly reflect emotional states with relatively low cost and simplicity, and it is suitable for either real-time or offline applications. This research aims to obtain and validate a Python-based method for extracting five emotion-related indicators from an EEG data stream. For this purpose, several different Python libraries are used, one of which is specifically developed for EEG and MEG signal analysis (mne-Python). The pipeline of code proposed by this study consists of the following steps: data import, pre-processing of the raw EEG signal, signal’s representation, band power computing, extraction of the chosen indicators, and indicators' representation.