Synthesizing Affective Neurophysiological Signals Using Generative Models: A Review Paper

TL;DR

This review explores how generative models can augment neurophysiological signals like EEG and fNIRS to improve emotion recognition systems, addressing data scarcity issues in affective computing.

Contribution

It provides a comprehensive analysis of generative models in neurophysiological data synthesis, highlighting methodologies, challenges, and future directions in emotion recognition.

Findings

Generative models effectively augment neurophysiological datasets.

Enhanced emotion recognition accuracy with synthesized data.

Identified challenges and promising future research avenues.

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.