In the context of designing a real-time brain-computer interface for playing a game using the OpenBCI Ultracortex "Mark IV" headset, this paper focuses on the work of the decoding subgroup. The primary responsibility is to analyse EEG data retrieved from the OpenBCI headset and classify the intention of the user. Our objective is to achieve a high-accuracy classification of the EEG signals. The paper is structured into three main sections: preprocessing, feature extraction, and classification. Multiple methods for preprocessing and classification of motor execution EEG signals will be analysed, striving to contribute to the real-time implementation of the project. The results of our work provides valuable insights for future research and development in this field.