  Within the field of Humanities, there is a recognized need for educational
innovation, as there are currently no reported tools available that enable
individuals to interact with their environment to create an enhanced learning
experience in the humanities (e.g., immersive spaces). This project proposes a
solution to address this gap by integrating technology and promoting the
development of teaching methodologies in the humanities, specifically by
incorporating emotional monitoring during the learning process of humanistic
context inside an immersive space. In order to achieve this goal, a real-time
emotion detection EEG-based system was developed to interpret and classify
specific emotions. These emotions aligned with the early proposal by Descartes
(Passions), including admiration, love, hate, desire, joy, and sadness. This
system aims to integrate emotional data into the Neurohumanities Lab
interactive platform, creating a comprehensive and immersive learning
environment. This work developed a ML, real-time emotion detection model that
provided Valence, Arousal, and Dominance (VAD) estimations every 5 seconds.
Using PCA, PSD, RF, and Extra-Trees, the best 8 channels and their respective
best band powers were extracted; furthermore, multiple models were evaluated
using shift-based data division and cross-validations. After assessing their
performance, Extra-Trees achieved a general accuracy of 96%, higher than the
reported in the literature (88% accuracy). The proposed model provided
real-time predictions of VAD variables and was adapted to classify Descartes'
six main passions. However, with the VAD values obtained, more than 15 emotions
can be classified (reported in the VAD emotion mapping) and extend the range of
this application.
