# Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification

arXiv: https://arxiv.org/abs/1901.07429 · HTML: https://mlsanity.com/paper/1901.07429/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2019.

## Built on by (most cited first, 12 of 12)

- [GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition](https://mlsanity.com/paper/2109.03124/) (2021)
- [EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders](https://mlsanity.com/paper/2207.08002/) (2022)
- [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)
- [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)
- [On the use of generative deep neural networks to synthesize artificial multichannel EEG signals](https://mlsanity.com/paper/2102.08061/) (2021)
- [Conditional Generative Models for Simulation of EMG During Naturalistic Movements](https://mlsanity.com/paper/2211.01856/) (2022)
- [vEEGNet: learning latent representations to reconstruct EEG raw data via variational autoencoders](https://mlsanity.com/paper/2312.09449/) (2023)
- [SSVEP-DAN: A Data Alignment Network for SSVEP-based Brain Computer Interfaces](https://mlsanity.com/paper/2311.12666/) (2023)
- [Short-length SSVEP data extension by a novel generative adversarial networks based framework](https://mlsanity.com/paper/2301.05599/) (2023)
- [On using AI for EEG-based BCI applications: problems, current challenges and future trends](https://mlsanity.com/paper/2506.16168/) (2025)

## Abstract (arXiv)

Despite significant recent progress in the area of Brain-Computer Interface (BCI), there are numerous shortcomings associated with collecting Electroencephalography (EEG) signals in real-world environments. These include, but are not limited to, subject and session data variance, long and arduous calibration processes and predictive generalisation issues across different subjects or sessions. This implies that many downstream applications, including Steady State Visual Evoked Potential (SSVEP) based classification systems, can suffer from a shortage of reliable data. Generating meaningful and realistic synthetic data can therefore be of significant value in circumventing this problem. We explore the use of modern neural-based generative models trained on a limited quantity of EEG data collected from different subjects to generate supplementary synthetic EEG signal vectors, subsequently utilised to train an SSVEP classifier. Extensive experimental analysis demonstrates the efficacy of our generated data, leading to improvements across a variety of evaluations, with the crucial task of cross-subject generalisation improving by over 35% with the use of such synthetic data.