# A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction

arXiv: https://arxiv.org/abs/2012.00430 · HTML: https://mlsanity.com/paper/2012.00430/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2020.

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

- [Data Augmentation for Seizure Prediction with Generative Diffusion Model](https://mlsanity.com/paper/2306.08256/) (2023)
- [EvoBrain: Dynamic Multi-Channel EEG Graph Modeling for Time-Evolving Brain Networks](https://mlsanity.com/paper/2509.15857/) (2025)
- [Long-Term EEG Partitioning for Seizure Onset Detection](https://mlsanity.com/paper/2412.15598/) (2024)
- [Multichannel Synthetic Preictal EEG Signals to Enhance the Prediction of Epileptic Seizures](https://mlsanity.com/paper/2205.03239/) (2022)
- [C2SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction](https://mlsanity.com/paper/2110.13674/) (2021)
- [SplitSEE: A Splittable Self-supervised Framework for Single-Channel EEG Representation Learning](https://mlsanity.com/paper/2410.11200/) (2024)
- [BISeizuRe: BERT-Inspired Seizure Data Representation to Improve Epilepsy Monitoring](https://mlsanity.com/paper/2406.19189/) (2024)
- [Preictal Period Optimization for Deep Learning-Based Epileptic Seizure Prediction](https://mlsanity.com/paper/2407.14876/) (2024)
- [Enhancing EEG Signal Generation through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models](https://mlsanity.com/paper/2410.00013/) (2024)
- [Synthetic ALS-EEG Data Augmentation for ALS Diagnosis Using Conditional WGAN with Weight Clipping](https://mlsanity.com/paper/2506.16243/) (2025)
- [GEPD:GAN-Enhanced Generalizable Model for EEG-Based Detection of Parkinson's Disease](https://mlsanity.com/paper/2508.14074/) (2025)
- [RNN-Based Models for Predicting Seizure Onset in Epileptic Patients](https://mlsanity.com/paper/2501.16334/) (2024)
- [A Statistical Approach for Synthetic EEG Data Generation](https://mlsanity.com/paper/2504.16143/) (2025)
- [Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions](https://mlsanity.com/paper/2603.12296/) (2026)

## Abstract (arXiv)

Prediction of seizure before they occur is vital for bringing normalcy to the lives of patients. Researchers employed machine learning methods using hand-crafted features for seizure prediction. However, ML methods are too complicated to select the best ML model or best features. Deep Learning methods are beneficial in the sense of automatic feature extraction. One of the roadblocks for accurate seizure prediction is scarcity of epileptic seizure data. This paper addresses this problem by proposing a deep convolutional generative adversarial network to generate synthetic EEG samples. We use two methods to validate synthesized data namely, one-class SVM and a new proposal which we refer to as convolutional epileptic seizure predictor (CESP). Another objective of our study is to evaluate performance of well-known deep learning models (e.g., VGG16, VGG19, ResNet50, and Inceptionv3) by training models on augmented data using transfer learning with average time of 10 min between true prediction and seizure onset. Our results show that CESP model achieves sensitivity of 78.11% and 88.21%, and FPR of 0.27/h and 0.14/h for training on synthesized and testing on real Epilepsyecosystem and CHB-MIT datasets, respectively. Effective results of CESP trained on synthesized data shows that synthetic data acquired the correlation between features and labels very well. We also show that employment of idea of transfer learning and data augmentation in patient-specific manner provides highest accuracy with sensitivity of 90.03% and 0.03 FPR/h which was achieved using Inceptionv3, and that augmenting data with samples generated from DCGAN increased prediction results of our CESP model and Inceptionv3 by 4-5% as compared to state-of-the-art traditional augmentation techniques. Finally, we note that prediction results of CESP achieved by using augmented data are better than chance level for both datasets.