Here we present a robust physiological model for the recognition of human emotions, called Deep Physiological Affect Network. This model is based on a convolutional long short-term memory (ConvLSTM) network and a new temporal margin-based loss function. Formulating the emotion recognition problem as a spectral-temporal sequence classification problem of bipolar EEG signals underlying brain lateralization and photoplethysmogram signals, the proposed model improves the performance of emotion recognition. Specifically, the new loss function allows the model to be more confident as it observes more of specific feelings while training ConvLSTM models. The function is designed to result in penalties for the violation of such confidence. Our experiments on a public dataset show that our deep physiological learning technology significantly increases the recognition rate of state-of-the-art techniques by 15.96 percent increase in accuracy. An extensive analysis of the relationship between participants’ emotion ratings and physiological changes in brain lateralization function during the experiment is also presented.

中文翻译：

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用于识别人类情绪的深层生理影响网络

在这里，我们提出了一种用于识别人类情绪的强大生理模型，称为深度生理影响网络。该模型基于卷积长短期记忆 (ConvLSTM) 网络和新的基于时间边际的损失函数。将情绪识别问题制定为基于大脑偏侧化和光电容积描记信号的双极 EEG 信号的频谱-时间序列分类问题，所提出的模型提高了情绪识别的性能。具体来说，新的损失函数让模型更加自信，因为它在训练 ConvLSTM 模型时观察到了更多的特定感受。该功能旨在对违反这种信任的行为进行处罚。我们在公共数据集上的实验表明，我们的深度生理学习技术显着提高了最先进技术的识别率，准确率提高了 15.96%。还对实验期间参与者的情绪评级与大脑侧化功能的生理变化之间的关系进行了广泛的分析。