How to efficiently tag the emotional experience of multimedia contents is an important and challenging problem in the field of affective computing. This paper presents an EEG-based real-time emotion tagging approach, by extracting inter-brain features from a group of participants when they watch the same emotional video clips. First, the continuous subjective reports on both the arousal and valence dimensions of emotion were obtained by employing a three-round behavioral rating paradigm. Second, the inter-brain features were systematically explored in both spectral and temporal domain. Finally, regression analyses were performed to evaluate the effectiveness of inter-brain amplitude and phase features. The inter-brain amplitude feature showed significantly better prediction performance than the inter-brain phase feature, as well as another two conventional features (spectral power and inter-subject correlation). By combining the four types of features, regression values (R ² ) were obtained for the prediction of arousal ( ${{0.61}}\pm {{0.01}}$ ) and valence ( ${{0.70}}\pm {{0.01}}$ ), corresponding to prediction errors of ${{1.01}}\pm {{0.02}}$ and ${{0.78}}\pm {{0.02}}$ (unit on 9-point scales), respectively. The contributions of different electrodes and frequency bands were also analyzed. Our results show promising potentials of inter-brain EEG features in real-time emotion tagging applications.

中文翻译：

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视频观看期间连续内隐情绪标记的脑间脑电特征提取和分析

如何有效地标记多媒体内容的情感体验是情感计算领域中一个重要且具有挑战性的问题。本文提出了一种基于脑电图的实时情感标记方法，方法是从一组参与者观看相同的情感视频剪辑时提取他们的大脑间特征。首先，通过采用三轮行为评级范例，获得了关于情绪的唤醒和效价维度的连续主观报告。其次，在谱域和时域领域都系统地研究了大脑间的特征。最后，进行回归分析以评估脑间振幅和相位特征的有效性。脑间幅度特征显示出比脑间相位特征好得多的预测性能，以及另外两个常规功能（频谱功率和对象间相关性）。通过组合四种类型的特征，回归值（R ² ）用来预测唤醒（ $ {{0.61}} \ pm {{0.01}} $ ）和价（ $ {{0.70}} \ pm {{0.01}} $ ），对应于 $ {{1.01}} \ pm {{0.02}} $ 和 $ {{0.78}} \ pm {{0.02}} $（以9分制为单位）。还分析了不同电极和频带的贡献。我们的结果表明，在实时情绪标记应用中脑间脑电图功能的潜力很大。