# On the use of generative deep neural networks to synthesize artificial multichannel EEG signals

arXiv: https://arxiv.org/abs/2102.08061 · HTML: https://mlsanity.com/paper/2102.08061/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 16 Feb 2021.

## Builds on (most cited first, 8 of 8)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals](https://mlsanity.com/paper/1806.01875/) (2018)
- [Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models](https://mlsanity.com/paper/2006.05331/) (2020)
- [Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification](https://mlsanity.com/paper/1901.07429/) (2019)
- [Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders](https://mlsanity.com/paper/1812.06857/) (2018)
- [Improving brain computer interface performance by data augmentation with conditional Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1806.07108/) (2018)
- [Synthetic Epileptic Brain Activities Using Generative Adversarial Networks](https://mlsanity.com/paper/1907.10518/) (2019)

## Built on by (most cited first, 2 of 2)

- [EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders](https://mlsanity.com/paper/2207.08002/) (2022)
- [A Statistical Approach for Synthetic EEG Data Generation](https://mlsanity.com/paper/2504.16143/) (2025)

## Abstract (arXiv)

Recent promises of generative deep learning lately brought interest to its potential uses in neural engineering. In this paper we firstly review recently emerging studies on generating artificial electroencephalography (EEG) signals with deep neural networks. Subsequently, we present our feasibility experiments on generating condition-specific multichannel EEG signals using conditional variational autoencoders. By manipulating real resting-state EEG epochs, we present an approach to synthetically generate time-series multichannel signals that show spectro-temporal EEG patterns which are expected to be observed during distinct motor imagery conditions.