# Improving brain computer interface performance by data augmentation with conditional Deep Convolutional Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1806.07108 · HTML: https://mlsanity.com/paper/1806.07108/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2018.

## Built on by (most cited first, 8 of 8)

- [Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface](https://mlsanity.com/paper/2102.04456/) (2021)
- [Covariance Density Neural Networks](https://mlsanity.com/paper/2505.11139/) (2025)
- [On the use of generative deep neural networks to synthesize artificial multichannel EEG signals](https://mlsanity.com/paper/2102.08061/) (2021)
- [Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognition](https://mlsanity.com/paper/2204.03737/) (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)
- [Improving EEG Signal Classification Accuracy Using Wasserstein Generative Adversarial Networks](https://mlsanity.com/paper/2402.09453/) (2024)
- [Graph Variate Neural Networks](https://mlsanity.com/paper/2509.20311/) (2025)
- [High-Resolution Dynamic Functional Connectivity Generation with Graph-Variate Flow Matching](https://mlsanity.com/paper/2609.37037/) (2026)

## Abstract (arXiv)

One of the big restrictions in brain computer interface field is the very limited training samples, it is difficult to build a reliable and usable system with such limited data. Inspired by generative adversarial networks, we propose a conditional Deep Convolutional Generative Adversarial (cDCGAN) Networks method to generate more artificial EEG signal automatically for data augmentation to improve the performance of convolutional neural networks in brain computer interface field and overcome the small training dataset problems. We evaluate the proposed cDCGAN method on BCI competition dataset of motor imagery. The results show that the generated artificial EEG data from Gaussian noise can learn the features from raw EEG data and has no less than the classification accuracy of raw EEG data in the testing dataset. Also by using generated artificial data can effectively improve classification accuracy at the same model with limited training data.