# EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals

arXiv: https://arxiv.org/abs/1806.01875 · HTML: https://mlsanity.com/paper/1806.01875/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2018.

## Built on by (most cited first, 30 of 48)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Data Augmentation techniques in time series domain: A survey and taxonomy](https://mlsanity.com/paper/2206.13508/) (2022)
- [CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals](https://mlsanity.com/paper/2106.13695/) (2021)
- [Generative adversarial networks in time series: A survey and taxonomy](https://mlsanity.com/paper/2107.11098/) (2021)
- [Generative Adversarial Networks: A Survey Towards Private and Secure Applications](https://mlsanity.com/paper/2106.03785/) (2021)
- [EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders](https://mlsanity.com/paper/2207.08002/) (2022)
- [A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution](https://mlsanity.com/paper/2111.08685/) (2021)
- [A Tale of Single-channel Electroencephalogram: Devices, Datasets, Signal Processing, Applications, and Future Directions](https://mlsanity.com/paper/2407.14850/) (2024)
- [Unveiling Thoughts: A Review of Advancements in EEG Brain Signal Decoding into Text](https://mlsanity.com/paper/2405.00726/) (2024)
- [Let EEG Models Learn EEG](https://mlsanity.com/paper/2605.21280/) (2026)
- [A Spectral Enabled GAN for Time Series Data Generation](https://mlsanity.com/paper/2103.01904/) (2021)
- [Time-Transformer: Integrating Local and Global Features for Better Time Series Generation (Extended Version)](https://mlsanity.com/paper/2312.11714/) (2023)
- [Multichannel Synthetic Preictal EEG Signals to Enhance the Prediction of Epileptic Seizures](https://mlsanity.com/paper/2205.03239/) (2022)
- [You Only Acquire Sparse-channel (YOAS): A Unified Framework for Dense-channel EEG Generation](https://mlsanity.com/paper/2406.15269/) (2024)
- [Large Language Models for EEG: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2506.06353/) (2025)
- [Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface](https://mlsanity.com/paper/2102.04456/) (2021)
- [Towards physiology-informed data augmentation for EEG-based BCIs](https://mlsanity.com/paper/2203.14392/) (2022)
- [Improving EEG Classification Through Randomly Reassembling Original and Generated Data with Transformer-based Diffusion Models](https://mlsanity.com/paper/2407.20253/) (2024)
- [An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works](https://mlsanity.com/paper/2105.14278/) (2021)
- [Impact of dataset size and long-term ECoG-based BCI usage on deep learning decoders performance](https://mlsanity.com/paper/2209.03789/) (2022)
- [Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendations](https://mlsanity.com/paper/2405.19345/) (2024)
- [On the use of generative deep neural networks to synthesize artificial multichannel EEG signals](https://mlsanity.com/paper/2102.08061/) (2021)
- [Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus](https://mlsanity.com/paper/2205.03231/) (2022)
- [Conditional Generative Models for Simulation of EMG During Naturalistic Movements](https://mlsanity.com/paper/2211.01856/) (2022)
- [SleepEGAN: A GAN-enhanced Ensemble Deep Learning Model for Imbalanced Classification of Sleep Stages](https://mlsanity.com/paper/2307.05362/) (2023)
- [Early Warning Prediction with Automatic Labeling in Epilepsy Patients](https://mlsanity.com/paper/2310.06059/) (2023)
- [Decoding Phone Pairs from MEG Signals Across Speech Modalities](https://mlsanity.com/paper/2505.15355/) (2025)
- [ECG synthesis with Neural ODE and GAN models](https://mlsanity.com/paper/2111.00314/) (2021)
- [Persistent Homological State-Space Estimation of Functional Human Brain Networks at Rest](https://mlsanity.com/paper/2201.00087/) (2022)
- [A privacy-preserving data storage and service framework based on deep learning and blockchain for construction workers' wearable IoT sensors](https://mlsanity.com/paper/2211.10713/) (2022)

## Abstract (arXiv)

Generative adversarial networks (GANs) are recently highly successful in generative applications involving images and start being applied to time series data. Here we describe EEG-GAN as a framework to generate electroencephalographic (EEG) brain signals. We introduce a modification to the improved training of Wasserstein GANs to stabilize training and investigate a range of architectural choices critical for time series generation (most notably up- and down-sampling). For evaluation we consider and compare different metrics such as Inception score, Frechet inception distance and sliced Wasserstein distance, together showing that our EEG-GAN framework generated naturalistic EEG examples. It thus opens up a range of new generative application scenarios in the neuroscientific and neurological context, such as data augmentation in brain-computer interfacing tasks, EEG super-sampling, or restoration of corrupted data segments. The possibility to generate signals of a certain class and/or with specific properties may also open a new avenue for research into the underlying structure of brain signals.