Emotions are one of the important components of the human being, thus they are a valuable part of daily activities such as interaction with people, decision making and learning. For this reason, it is important to detect, recognize and understand emotions using computational systems to improve communication between people and machines, which would facilitate the ability of computers to understand the communication between humans. This study proposes the creation of a model that allows the classification of people's emotions based on their EEG signals, for which the brain-computer interface EMOTIV EPOC was used. This allowed the collection of electroencephalographic information from 50 people, all of whom were shown audiovisual resources that helped to provoke the desired mood. The information obtained was stored in a database for the generation of the model and the corresponding classification analysis. Random Forest model was created for emotion prediction (happiness, sadness and relaxation), based on the signals of any person. The results obtained were 97.21% accurate for happiness, 76% for relaxation and 76% for sadness. Finally, the model was used to generate a real-time emotion prediction algorithm; it captures the person's EEG signals, executes the generated algorithm and displays the result on the screen with the help of images representative of each emotion.

翻译：情感是人类的重要组成部分，因此它们在日常活动中具有重要价值，如人际互动、决策制定和学习过程。因此，利用计算系统检测、识别和理解情感对于改善人机交互至关重要，这将提升计算机理解人类交流的能力。本研究提出构建一种基于脑电图信号对人类情感进行分类的模型，为此采用了EMOTIV EPOC脑机接口设备。该设备采集了50名受试者的脑电图数据，所有受试者均观看了旨在诱发目标情绪（快乐、悲伤、放松）的视听材料。获取的数据存储于数据库中，用于模型构建及相应的分类分析。研究基于任意个体的脑电信号建立了随机森林模型进行情感预测（快乐、悲伤、放松）。所得结果的准确率分别为：快乐97.21%、放松76%、悲伤76%。最终，该模型被用于开发实时情感预测算法：系统捕获个体的脑电信号，执行训练生成的算法，并通过代表各情绪的图标在屏幕上可视化呈现预测结果。