Emotions are fundamental for human beings and play an important role in human cognition. Emotion is commonly associated with logical decision making, perception, human interaction, and to a certain extent, human intelligence itself. With the growing interest of the research community towards establishing some meaningful “emotional” interactions between humans and computers, the need for reliable and deployable solutions for the identification of human emotional states is required. Recent developments in using electroencephalography (EEG) for emotion recognition have garnered strong interest from the research community as the latest developments in consumer-grade wearable EEG solutions can provide a cheap, portable, and simple solution for identifying emotions. Since the last comprehensive review was conducted back from the years 2009 to 2016, this paper will update on the current progress of emotion recognition using EEG signals from 2016 to 2019. The focus on this state-of-the-art review focuses on the elements of emotion stimuli type and presentation approach, study size, EEG hardware, machine learning classifiers, and classification approach. From this state-of-the-art review, we suggest several future research opportunities including proposing a different approach in presenting the stimuli in the form of virtual reality (VR). To this end, an additional section devoted specifically to reviewing only VR studies within this research domain is presented as the motivation for this proposed new approach using VR as the stimuli presentation device. This review paper is intended to be useful for the research community working on emotion recognition using EEG signals as well as for those who are venturing into this field of research.

中文翻译：

---

基于脑电图的情绪识别：对当前趋势和机遇的最新回顾

情感对人类至关重要，在人类认知中起着重要作用。情感通常与逻辑决策，知觉，人类互动以及某种程度上的人类智力本身相关。随着研究界对在人与计算机之间建立有意义的“情感”互动的兴趣日益浓厚，需要有可靠且可部署的解决方案来识别人的情绪状态。使用脑电图（EEG）进行情感识别的最新发展引起了研究界的强烈兴趣，因为消费级可穿戴EEG解决方案的最新发展可以提供一种廉价，便携式和简单的识别情感的解决方案。自上一次全面审核是从2009年到2016年进行的，本文将介绍2016年至2019年使用EEG信号进行情感识别的最新进展。本最新综述的重点是情感刺激类型和呈现方法，研究规模，EEG硬件，机器的要素学习分类器和分类方法。通过此最新的综述，我们提出了一些未来的研究机会，包括提出一种以虚拟现实（VR）形式呈现刺激的不同方法。为此，提出了专门讨论该研究领域内仅VR研究的另一部分，作为采用VR作为刺激呈现设备的新方法的动机。