StanlEEG is an interactive 3D video game that responds directly to the players mood and fatigue levels. By processing data retrieved from an EEG to interpret emotion (positive, negative, neutral) and fatigue (0 to 10) using python ML in order to alter the terrain to correlate with the player's mental state. The game itself was created in Unity, with imported custom made 3D models. This game is a new interactive and functional way to visualize a person's mood. Our goal is to better help people understand their own state of mind and encourage introspection in a friendly, easily accessible way. StanlEEG puts an entertaining spin on taking care of yourself.
In order to play, download the StanleySim folder and run it in Unity 2022.1.11f1.
Citations:
Ashford, J., Bird, J.J., Campelo, F., Faria, D.R. (2020). Classification of EEG Signals Based on Image Representation of Statistical Features. In: Ju, Z., Yang, L., Yang, C., Gegov, A., Zhou, D. (eds) Advances in Computational Intelligence Systems. UKCI 2019. Advances in Intelligent Systems and Computing, vol 1043. Springer, Cham. https://doi.org/10.1007/978-3-030-29933-0_37
Grégoire Cattan, Pedro L. C. Rodrigues, & Marco Congedo. (2018). EEG Alpha Waves dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.2348892.
J. J. Bird, A. Ekart, C. D. Buckingham, and D. R. Faria, “Mental emotional sentiment classification with an eeg-based brain-machine interface,” in The International Conference on Digital Image and Signal Processing (DISP’19), Springer, 2019.
J. J. Bird, L. J. Manso, E. P. Ribiero, A. Ekart, and D. R. Faria, “A study on mental state classification using eeg-based brain-machine interface,”in 9th International Conference on Intelligent Systems, IEEE, 2018.