Abstract：
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.
摘要：
情感智能在机器中的集成是推进人机交互的重要一步。这就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提出了挑战。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了全面分析，检查他们的输入公式，部署战略，和评估综合数据质量的方法。这篇综述是一个全面的概述，提供对优势的见解，挑战,以及未来生成模型在情感识别系统中的应用方向。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，从而支持更有效和可靠的情感识别系统的发展。
