  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.
