Human emotion recognition has been a major field of research in the last decades owing to its noteworthy academic and industrial applications. However, most of the state-of-the-art methods identified emotions after analyzing facial images. Emotion recognition using electroencephalogram (EEG) signals has got less attention. However, the advantage of using EEG signals is that it can capture real emotion. However, very few EEG signals databases are publicly available for affective computing. In this work, we present a database consisting of EEG signals of 44 volunteers. Twenty-three out of forty-four are females. A 32 channels CLARITY EEG traveler sensor is used to record four emotional states namely, happy, fear, sad, and neutral of subjects by showing 12 videos. So, 3 video files are devoted to each emotion. Participants are mapped with the emotion that they had felt after watching each video. The recorded EEG signals are considered further to classify four types of emotions based on discrete wavelet transform and extreme learning machine (ELM) for reporting the initial benchmark classification performance. The ELM algorithm is used for channel selection followed by subband selection. The proposed method performs the best when features are captured from the gamma subband of the FP1-F7 channel with 94.72% accuracy. The presented database would be available to the researchers for affective recognition applications.

中文翻译：

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EEG数据库及其初始基准情感分类性能。

由于其在学术上和工业上的广泛应用，近几十年来，人类情感识别一直是研究的主要领域。但是，大多数最先进的方法都是在分析面部图像后识别情绪的。使用脑电图（EEG）信号进行情感识别的关注度下降。但是，使用EEG信号的优势在于它可以捕获真实的情绪。但是，很少有EEG信号数据库可公开用于情感计算。在这项工作中，我们提出了一个数据库，其中包含44名志愿者的脑电信号。四十四位女性中有二十三位是女性。一个32通道的CLARITY EEG旅行者传感器用于通过显示12个视频来记录四个情绪状态，即对象的快乐，恐惧，悲伤和中立。因此，每种情感都有3个视频文件。在观看每个视频后，参与者将感受到他们的情感。基于离散小波变换和极限学习机（ELM），可以考虑将记录的EEG信号进一步分类为四种情绪，以报告初始基准分类性能。ELM算法用于信道选择，然后进行子带选择。当以FP1-F7通道的伽马子带捕获特征时，以94.72％的精度，该方法的性能最佳。所提供的数据库可供研究人员用于情感识别应用。基于离散小波变换和极限学习机（ELM），可考虑将记录的EEG信号进一步分类为四种情绪，以报告初始基准分类性能。ELM算法用于信道选择，然后进行子带选择。当以FP1-F7通道的伽马子带捕获特征时，以94.72％的精度，该方法的性能最佳。提出的数据库将可供研究人员用于情感识别应用。基于离散小波变换和极限学习机（ELM），可考虑将记录的EEG信号进一步分类为四种情绪，以报告初始基准分类性能。ELM算法用于信道选择，然后进行子带选择。当以FP1-F7通道的伽马子带捕获特征时，以94.72％的精度，该方法的性能最佳。提出的数据库将可供研究人员用于情感识别应用。