用脑电图设备识别三类情绪，准确率达97%。
Emotion classification using EEG headset signals and Random Forest
- 基于脑电帽采集数据，用随机森林模型分类情绪
- 对快乐、放松、悲伤的识别准确率分别为97.21%、76%、76%
- 可实时输出情绪结果，适合人机交互场景
情绪是人类活动的重要组成部分，影响人际互动、决策与学习。为提升人机通信能力，需通过计算系统识别与理解情绪。本研究利用脑机接口EMOTIV EPOC采集50名受试者在视听刺激下的脑电图信号，构建数据库并训练随机森林模型，实现对快乐、悲伤和放松三类情绪的分类。结果显示，快乐识别准确率为97.21%，放松与悲伤均为76%。最终开发出实时情绪预测算法，可捕捉脑电信号并以图像形式实时显示情绪状态。
原文摘要 · Abstract (English)
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.
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