# Synthesizing Affective Neurophysiological Signals Using Generative Models: A Review Paper

arXiv: https://arxiv.org/abs/2306.03112 · HTML: https://mlsanity.com/paper/2306.03112/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 69 references that are in the map.
- First posted on arXiv 5 Jun 2023.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Autoencoding beyond pixels using a learned similarity metric](https://mlsanity.com/paper/1512.09300/) (2015)
- [A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances](https://mlsanity.com/paper/2203.06935/) (2022)
- [Dual Discriminator Generative Adversarial Nets](https://mlsanity.com/paper/1709.03831/) (2017)
- [EEG based Emotion Recognition: A Tutorial and Review](https://mlsanity.com/paper/2203.11279/) (2022)
- [Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models](https://mlsanity.com/paper/2006.05331/) (2020)
- [GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition](https://mlsanity.com/paper/2109.03124/) (2021)
- [EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders](https://mlsanity.com/paper/2207.08002/) (2022)
- [Progress in Brain Computer Interfaces: Challenges and Trends](https://mlsanity.com/paper/1901.03442/) (2019)
- [PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition](https://mlsanity.com/paper/2202.05400/) (2022)

## Built on by (most cited first, 1 of 1)

- [Enhancing Fatigue Detection through Heterogeneous Multi-Source Data Integration and Cross-Domain Modality Imputation](https://mlsanity.com/paper/2507.16859/) (2025)

## Abstract (arXiv)

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.