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

TL;DR

This paper presents a novel supervised neural gas network method for generating synthetic physiological emotion data, addressing data scarcity issues with faster processing and comparable accuracy to existing models.

Contribution

Introduces a Supervised Neural Gas network for synthetic data generation in emotion recognition, offering speed advantages and effective data distribution modeling.

Findings

Outperforms most evaluated models in accuracy.

Offers significant processing time improvements.

Generates data closely matching original distributions.

Abstract

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.