Multimodel emotion analysis in response to multimedia

Wei-Long Zheng, Jia-Yi Zhu, Bao-Liang Lu
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引用次数: 2

Abstract

In this demo paper, we designed a novel framework combining EEG and eye tracking signals to analyze users' emotional activities in response to multimedia. To realize the proposed framework, we extracted efficient features of EEG and eye tracking signals and used support vector machine as classifier. We combined multimodel features using feature-level fusion and decision-level fusion to classify three emotional categories (positive, neutral and negative), which can achieve the average accuracies of 75.62% and 74.92%, respectively. We investigated the brain activities that are associated with emotions. Our experimental results indicated there exist stable common patterns and activated areas of the brain associated with positive and negative emotions. In the demo, we also showed the trajectory of emotion changes in response to multimedia.
多媒体响应的多模型情感分析
在这篇演示论文中,我们设计了一个结合脑电图和眼动追踪信号的新框架来分析用户对多媒体的情感活动。为了实现该框架,我们提取了脑电图和眼动追踪信号的有效特征,并使用支持向量机作为分类器。我们结合多模型特征,采用特征级融合和决策级融合对正面、中性和负面三种情绪类别进行分类,平均准确率分别达到75.62%和74.92%。我们研究了与情绪相关的大脑活动。我们的实验结果表明,大脑中存在稳定的共同模式和与积极和消极情绪相关的激活区域。在演示中,我们还展示了情绪变化对多媒体的反应轨迹。
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