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Analyzing the dynamics of emotional scene sequence using recurrent neuro-fuzzy network

机译:使用递归神经模糊网络分析情绪场景序列的动力学

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摘要

In this paper, we propose a new framework to analyze the temporal dynamics of the emotional stimuli. For this framework, both electroencephalography signal and visual information are of great importance. The fusion of visual information with brain signals allows us to capture the users’ emotional state. Thus we adopt previously proposed fuzzy-GIST as emotional feature to summarize the emotional feedback. In order to model the dynamics of the emotional stimuli sequence, we develop a recurrent neuro-fuzzy network for modeling the dynamic events of emotional dimensions including valence and arousal. It can incorporate human expertise by IF-THEN fuzzy rule while recurrent connections allow the fuzzy rules of network to see its own previous output. The results show that such a framework can interact with human subjects and generate arbitrary emotional sequences after learning the dynamics of an emotional sequence with enough number of samples.
机译:在本文中,我们提出了一个新的框架来分析情绪刺激的时间动态。对于此框架,脑电图信号和视觉信息都非常重要。视觉信息与大脑信号的融合使我们能够捕获用户的情绪状态。因此,我们采用先前提出的模糊GIST作为情感特征来总结情感反馈。为了对情绪刺激序列的动力学建模,我们开发了一个递归神经模糊网络,用于对情绪维(包括价和唤醒)的动态事件进行建模。它可以通过IF-THEN模糊规则来整合人类的专业知识,而循环连接可以使网络的模糊规则看到自己的先前输出。结果表明,在学习了足够数量的样本的情绪序列动态之后,这样的框架可以与人类对象交互并生成任意的情绪序列。

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