# Enhancing EEG Signal-Based Emotion Recognition with Synthetic Data: Diffusion Model Approach

arXiv: https://arxiv.org/abs/2401.16878 · HTML: https://mlsanity.com/paper/2401.16878/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2024.

## Built on by (most cited first, 6 of 6)

- [Improving EEG Classification Through Randomly Reassembling Original and Generated Data with Transformer-based Diffusion Models](https://mlsanity.com/paper/2407.20253/) (2024)
- [Synthetic Data Generation by Supervised Neural Gas Network for Physiological Emotion Recognition Data](https://mlsanity.com/paper/2501.16353/) (2025)
- [Cross-domain EEG-based Emotion Recognition with Contrastive Learning](https://mlsanity.com/paper/2511.05293/) (2025)
- [Modified TSception for Analyzing Driver Drowsiness and Mental Workload from EEG](https://mlsanity.com/paper/2512.21747/) (2025)
- [A Statistical Approach for Synthetic EEG Data Generation](https://mlsanity.com/paper/2504.16143/) (2025)
- [SASG-DA: Sparse-Aware Semantic-Guided Diffusion Augmentation For Myoelectric Gesture Recognition](https://mlsanity.com/paper/2511.08344/) (2025)

## Abstract (arXiv)

Emotions are crucial in human life, influencing perceptions, relationships, behaviour, and choices. Emotion recognition using Electroencephalography (EEG) in the Brain-Computer Interface (BCI) domain presents significant challenges, particularly the need for extensive datasets. This study aims to generate synthetic EEG samples similar to real samples but distinct by augmenting noise to a conditional denoising diffusion probabilistic model, thus addressing the prevalent issue of data scarcity in EEG research. The proposed method is tested on the DEAP and SADT datasets, showcasing up to 5.6% improvement in classification accuracy when using synthetic data with DEAP and similar positive results with SADT. This is higher compared to the traditional Generative Adversarial Network (GAN) based and Denoising Diffusion Probabilistic Model (DDPM) based approaches. This study further evaluates the effectiveness of state-of-the-art classifiers on EEG data, employing both real and synthetic data with varying noise levels, and utilizes t-SNE and SHAP for detailed analysis and interpretability. The proposed diffusion-based approach for EEG data generation appears promising in refining the accuracy of emotion recognition systems and marks a notable contribution to EEG-based emotion recognition.