标题： PHemoNet：一种用于生理信号的多模态网络 显示英文标题

标题： PHemoNet: A Multimodal Network for Physiological Signals

摘要： 情感识别在包括医疗应用和脑机接口（BCI）在内的众多领域中至关重要。情感反应包括行为反应，如语调和身体运动，以及生理信号的变化，如脑电图（EEG）。后者是无意识的，因此它们提供了可靠的情绪识别输入，而与之相反的是，前者可以由个体有意识地控制。这些信号揭示了真实的情感状态，而没有故意改变，从而提高了情感识别模型的准确性。然而，来自生理信号的多模态深度学习方法尚未得到显著研究。在本文中，我们介绍了PHemoNet，这是一种用于从生理信号中进行多模态情感识别的完全超复网络。具体来说，该架构包括模态特定编码器和融合模块。这两个编码器和融合模块通过参数化超复乘法（PHMs）定义在超复域中，能够捕捉每种模态的不同维度之间以及模态之间的潜在关系。所提出的方法在使用脑电图（EEG）和外周生理信号分类效价和唤醒度方面优于当前最先进的模型在MAHNOB-HCI数据集上的表现。这项工作的代码可以在https://github.com/ispamm/MHyEEG获得。 显示英文摘要

摘要： Emotion recognition is essential across numerous fields, including medical applications and brain-computer interface (BCI). Emotional responses include behavioral reactions, such as tone of voice and body movement, and changes in physiological signals, such as the electroencephalogram (EEG). The latter are involuntary, thus they provide a reliable input for identifying emotions, in contrast to the former which individuals can consciously control. These signals reveal true emotional states without intentional alteration, thus increasing the accuracy of emotion recognition models. However, multimodal deep learning methods from physiological signals have not been significantly investigated. In this paper, we introduce PHemoNet, a fully hypercomplex network for multimodal emotion recognition from physiological signals. In detail, the architecture comprises modality-specific encoders and a fusion module. Both encoders and fusion modules are defined in the hypercomplex domain through parameterized hypercomplex multiplications (PHMs) that can capture latent relations between the different dimensions of each modality and between the modalities themselves. The proposed method outperforms current state-of-the-art models on the MAHNOB-HCI dataset in classifying valence and arousal using electroencephalograms (EEGs) and peripheral physiological signals. The code for this work is available at https://github.com/ispamm/MHyEEG.