# Synthesizing EEG Signals from Event-Related Potential Paradigms with Conditional Diffusion Models

arXiv: https://arxiv.org/abs/2403.18486 · HTML: https://mlsanity.com/paper/2403.18486/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 7 of 7)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models](https://mlsanity.com/paper/2301.12661/) (2023)
- [Data Augmentation for Seizure Prediction with Generative Diffusion Model](https://mlsanity.com/paper/2306.08256/) (2023)
- [EEG Synthetic Data Generation Using Probabilistic Diffusion Models](https://mlsanity.com/paper/2303.06068/) (2023)

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

- [A Survey on Diffusion Models for Time Series and Spatio-Temporal Data](https://mlsanity.com/paper/2404.18886/) (2024)
- [Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendations](https://mlsanity.com/paper/2405.19345/) (2024)
- [ArtifactGen: Benchmarking WGAN-GP vs Diffusion for Label-Aware EEG Artifact Synthesis](https://mlsanity.com/paper/2509.08188/) (2025)
- [Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions](https://mlsanity.com/paper/2603.12296/) (2026)
- [DECODE: Dual-Enhanced Conditioned Diffusion for EEG Forecasting](https://mlsanity.com/paper/2603.16885/) (2026)
- [Sensor Geometry as a Flow-Matching Prior for Multi-Channel Brain Signals](https://mlsanity.com/paper/2610.08355/) (2026)

## Abstract (arXiv)

Data scarcity in the brain-computer interface field can be alleviated through the use of generative models, specifically diffusion models. While diffusion models have previously been successfully applied to electroencephalogram (EEG) data, existing models lack flexibility w.r.t.~sampling or require alternative representations of the EEG data. To overcome these limitations, we introduce a novel approach to conditional diffusion models that utilizes classifier-free guidance to directly generate subject-, session-, and class-specific EEG data. In addition to commonly used metrics, domain-specific metrics are employed to evaluate the specificity of the generated samples. The results indicate that the proposed model can generate EEG data that resembles real data for each subject, session, and class.