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

Abstract

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.

arXiv comment4 pages, 5 figures

References in corpus (5)

• Conditional Generative Adversarial Nets
• Deep learning with convolutional neural networks for EEG decoding and visualization
• Generative Adversarial Text to Image Synthesis
• Generating Videos with Scene Dynamics
• How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?

Cited by in corpus (3)

• Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification
• Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface
• A Survey on Brain-Computer Interface and Related Applications