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.

翻译：在人文学科领域，教育创新需求日益凸显，目前尚无公开工具能帮助个体与环境交互以创造增强型人文学科学习体验（如沉浸式空间）。本项目提出解决方案，通过整合技术并推动人文学科教学方法发展，特别在沉浸式空间的人文情境学习过程中引入情绪监测机制。为实现该目标，研发了一套基于脑电图（EEG）的实时情绪检测系统，用于解读和分类特定情绪。这些情绪与笛卡尔早期提出的"六种原初情感"（惊叹、爱、恨、欲望、喜悦、悲伤）相契合。该系统旨在将情绪数据整合至神经人文学实验室交互平台，构建全面沉浸式学习环境。本研究开发了基于机器学习的实时情绪检测模型，每5秒输出效价、唤醒度与支配度（VAD）估计值。通过主成分分析、功率谱密度、随机森林和极端随机树算法，提取最优的8个通道及其对应最佳频段功率；采用基于滑动窗口的数据划分与交叉验证方法评估多个模型性能。经性能评估，极端随机树模型达到96%的总体准确率，高于文献报道的88%准确率阈值。该模型不仅提供VAD变量的实时预测，还能适配分类笛卡尔的六种基本情感。借助获取的VAD值，可进一步识别超过15种情绪（依据VAD情绪映射图），从而拓展该应用的范围。