# Synthetic Data Generation by Supervised Neural Gas Network for Physiological Emotion Recognition Data

arXiv: https://arxiv.org/abs/2501.16353 · HTML: https://mlsanity.com/paper/2501.16353/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 51 references that are in the map.
- First posted on arXiv 19 Jan 2025.

## Builds on (most cited first, 12 of 12)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks](https://mlsanity.com/paper/1512.05287/) (2015)
- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [Comprehensive Exploration of Synthetic Data Generation: A Survey](https://mlsanity.com/paper/2401.02524/) (2024)
- [TTS-CGAN: A Transformer Time-Series Conditional GAN for Biosignal Data Augmentation](https://mlsanity.com/paper/2206.13676/) (2022)
- [Speech Emotion Recognition using Supervised Deep Recurrent System for Mental Health Monitoring](https://mlsanity.com/paper/2208.12812/) (2022)
- [Synthetic ECG Signal Generation Using Generative Neural Networks](https://mlsanity.com/paper/2112.03268/) (2021)
- [Enhancing EEG Signal-Based Emotion Recognition with Synthetic Data: Diffusion Model Approach](https://mlsanity.com/paper/2401.16878/) (2024)
- [Neural Gas Network Image Features and Segmentation for Brain Tumor Detection Using Magnetic Resonance Imaging Data](https://mlsanity.com/paper/2301.12176/) (2023)
- [Bees Local Phase Quantization Feature Selection for RGB-D Facial Expressions Recognition](https://mlsanity.com/paper/2308.01700/) (2023)
- [PSO Fuzzy XGBoost Classifier Boosted with Neural Gas Features on EEG Signals in Emotion Recognition](https://mlsanity.com/paper/2407.09950/) (2024)

## Built on by (most cited first, 4 of 4)

- [Synthetic Data Generation of Body Motion Data by Neural Gas Network for Emotion Recognition](https://mlsanity.com/paper/2503.14513/) (2025)
- [Synthetic Data Generation for Emotional Depth Faces: Optimizing Conditional DCGANs via Genetic Algorithms in the Latent Space and Stabilizing Training with Knowledge Distillation](https://mlsanity.com/paper/2508.09188/) (2025)
- [MVRS: The Multimodal Virtual Reality Stimuli-based Emotion Recognition Dataset](https://mlsanity.com/paper/2509.05330/) (2025)
- [Generative Learning as a Tool to Improve Perception of Emotional Body Motion Expressions](https://mlsanity.com/paper/2606.28769/) (2026)

## Abstract (arXiv)

Data scarcity remains a significant challenge in the field of emotion recognition using physiological signals, as acquiring comprehensive and diverse datasets is often prevented by privacy concerns and logistical constraints. This limitation restricts the development and generalization of robust emotion recognition models, making the need for effective synthetic data generation methods more critical. Emotion recognition from physiological signals such as EEG, ECG, and GSR plays a pivotal role in enhancing human-computer interaction and understanding human affective states. Utilizing these signals, this study introduces an innovative approach to synthetic data generation using a Supervised Neural Gas (SNG) network, which has demonstrated noteworthy speed advantages over established models like Conditional VAE, Conditional GAN, diffusion model, and Variational LSTM. The Neural Gas network, known for its adaptability in organizing data based on topological and feature-space proximity, provides a robust framework for generating real-world-like synthetic datasets that preserve the intrinsic patterns of physiological emotion data. Our implementation of the SNG efficiently processes the input data, creating synthetic instances that closely mimic the original data distributions, as demonstrated through comparative accuracy assessments. In experiments, while our approach did not universally outperform all models, it achieved superior performance against most of the evaluated models and offered significant improvements in processing time. These outcomes underscore the potential of using SNG networks for fast, efficient, and effective synthetic data generation in emotion recognition applications.