With the development of sensing technologies and machine learning, techniques that can identify emotions and inner states of a human through physiological signals, known as electroencephalography (EEG), have been actively developed and applied to various domains, such as automobiles, robotics, healthcare, and customer-support services. Thus, the demand for acquiring and analyzing EEG signals in real-time is increasing. In this paper, we aimed to acquire a new EEG dataset based on the discrete emotion theory, termed as WeDea (Wireless-based eeg Data for emotion analysis), and propose a new combination for WeDea analysis. For the collected WeDea dataset, we used video clips as emotional stimulants that were selected by 15 volunteers. Consequently, WeDea is a multi-way dataset measured while 30 subjects are watching the selected 79 video clips under five different emotional states using a convenient portable headset device. Furthermore, we designed a framework for recognizing human emotional state using this new database. The practical results for different types of emotions have proven that WeDea is a promising resource for emotion analysis and can be applied to the field of neuroscience.

中文翻译：

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WeDea：一种基于脑电图的新情绪识别框架


随着传感技术和机器学习的发展，通过生理信号识别人类情绪和内心状态的技术，即脑电图（EEG），已被积极开发并应用于各个领域，例如汽车、机器人、医疗保健、和客户支持服务。因此，实时采集和分析脑电信号的需求不断增加。在本文中，我们的目标是获取一个基于离散情感理论的新脑电数据集，称为WeDea（基于无线的脑电数据进行情感分析），并提出一种新的WeDea分析组合。对于收集的 WeDea 数据集，我们使用 15 名志愿者选择的视频片段作为情绪兴奋剂。因此，WeDea 是一个多路数据集，测量 30 名受试者使用方便的便携式耳机设备在五种不同情绪状态下观看选定的 79 个视频剪辑。此外，我们设计了一个使用这个新数据库识别人类情绪状态的框架。不同类型情绪的实际结果证明，WeDea 是一种很有前景的情绪分析资源，可以应用于神经科学领域。