# Data Augmentation for Seizure Prediction with Generative Diffusion Model

arXiv: https://arxiv.org/abs/2306.08256 · HTML: https://mlsanity.com/paper/2306.08256/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 25 of its 81 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Placed in

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

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Effective Data Augmentation With Diffusion Models](https://mlsanity.com/paper/2302.07944/) (2023)
- [Data Augmentation Generative Adversarial Networks](https://mlsanity.com/paper/1711.04340/) (2017)
- [Brain Imaging Generation with Latent Diffusion Models](https://mlsanity.com/paper/2209.07162/) (2022)
- [RoentGen: Vision-Language Foundation Model for Chest X-ray Generation](https://mlsanity.com/paper/2211.12737/) (2022)
- [MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion](https://mlsanity.com/paper/2203.12621/) (2022)
- [Few-Shot Diffusion Models](https://mlsanity.com/paper/2205.15463/) (2022)
- [Fine-grained Categorization and Dataset Bootstrapping using Deep Metric Learning with Humans in the Loop](https://mlsanity.com/paper/1512.05227/) (2015)
- [Focal onset seizure prediction using convolutional networks](https://mlsanity.com/paper/1805.11576/) (2018)
- [A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction](https://mlsanity.com/paper/2012.00430/) (2020)
- [Toward Open-World Electroencephalogram Decoding Via Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2112.06654/) (2021)
- [An End-to-End Deep Learning Approach for Epileptic Seizure Prediction](https://mlsanity.com/paper/2108.07453/) (2021)
- [Multichannel Synthetic Preictal EEG Signals to Enhance the Prediction of Epileptic Seizures](https://mlsanity.com/paper/2205.03239/) (2022)
- [Staging Epileptogenesis with Deep Neural Networks](https://mlsanity.com/paper/2006.09885/) (2020)

## Built on by (most cited first, 14 of 14)

- [A Survey on Diffusion Models for Time Series and Spatio-Temporal Data](https://mlsanity.com/paper/2404.18886/) (2024)
- [CapRL: Stimulating Dense Image Caption Capabilities via Reinforcement Learning](https://mlsanity.com/paper/2509.22647/) (2025)
- [LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis](https://mlsanity.com/paper/2604.28178/) (2026)
- [Let EEG Models Learn EEG](https://mlsanity.com/paper/2605.21280/) (2026)
- [Synthesizing EEG Signals from Event-Related Potential Paradigms with Conditional Diffusion Models](https://mlsanity.com/paper/2403.18486/) (2024)
- [Improving EEG Classification Through Randomly Reassembling Original and Generated Data with Transformer-based Diffusion Models](https://mlsanity.com/paper/2407.20253/) (2024)
- [EEG-DIF: Early Warning of Epileptic Seizures through Generative Diffusion Model-based Multi-channel EEG Signals Forecasting](https://mlsanity.com/paper/2410.17343/) (2024)
- [EEG-based Epileptic Prediction via a Two-stage Channel-aware Set Transformer Network](https://mlsanity.com/paper/2507.15364/) (2025)
- [Geometry- and Relation-Aware Diffusion for EEG Super-Resolution](https://mlsanity.com/paper/2602.02238/) (2026)
- [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)
- [DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data](https://mlsanity.com/paper/2605.17866/) (2026)
- [Not All EEG Moments Are Equal: Position-Adaptive Time Scheduling for EEG Generation](https://mlsanity.com/paper/2608.00048/) (2026)
- [MontageFlow: Physically Valid High Fidelity EEG Generation](https://mlsanity.com/paper/2610.14507/) (2026)

## Abstract (arXiv)

Data augmentation (DA) can significantly strengthen the electroencephalogram (EEG)-based seizure prediction methods. However, existing DA approaches are just the linear transformations of original data and cannot explore the feature space to increase diversity effectively. Therefore, we propose a novel diffusion-based DA method called DiffEEG. DiffEEG can fully explore data distribution and generate samples with high diversity, offering extra information to classifiers. It involves two processes: the diffusion process and the denoised process. In the diffusion process, the model incrementally adds noise with different scales to EEG input and converts it into random noise. In this way, the representation of data can be learned. In the denoised process, the model utilizes learned knowledge to sample synthetic data from random noise input by gradually removing noise. The randomness of input noise and the precise representation enable the synthetic samples to possess diversity while ensuring the consistency of feature space. We compared DiffEEG with original, down-sampling, sliding windows and recombination methods, and integrated them into five representative classifiers. The experiments demonstrate the effectiveness and generality of our method. With the contribution of DiffEEG, the Multi-scale CNN achieves state-of-the-art performance, with an average sensitivity, FPR, AUC of 95.4%, 0.051/h, 0.932 on the CHB-MIT database and 93.6%, 0.121/h, 0.822 on the Kaggle database.