The integration of emotional intelligence in machines is an important step in advancing human–computer interaction. This demands the development of reliable end-to-end emotion recognition systems. However, the scarcity of public affective datasets presents a challenge. In this literature review, we emphasize the use of generative models to address this issue in neurophysiological signals, particularly Electroencephalogram (EEG) and Functional Near-Infrared Spectroscopy (fNIRS). We provide a comprehensive analysis of different generative models used in the field, examining their input formulation, deployment strategies, and methodologies for evaluating the quality of synthesized data. This review serves as a comprehensive overview, offering insights into the advantages, challenges, and promising future directions in the application of generative models in emotion recognition systems. Through this review, we aim to facilitate the progression of neurophysiological data augmentation, thereby supporting the development of more efficient and reliable emotion recognition systems.

中文翻译：

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使用生成模型合成情感神经生理信号：综述论文

将情感智能融入机器是推进人机交互的重要一步。这需要开发可靠的端到端情感识别系统。然而，公共情感数据集的稀缺带来了挑战。在这篇文献综述中，我们强调使用生成模型来解决神经生理信号中的这个问题，特别是脑电图（EEG）和功能近红外光谱（fNIRS）。我们对该领域使用的不同生成模型进行全面分析，检查其输入公式、部署策略以及评估合成数据质量的方法。这篇综述作为一个全面的概述，深入探讨了生成模型在情感识别系统中的应用的优势、挑战和有希望的未来方向。通过这次审查，我们的目标是促进神经生理学数据增强的进展，从而支持更高效、更可靠的情绪识别系统的开发。