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

TL;DR

This paper introduces a conditional deep convolutional GAN to generate artificial EEG data, enhancing brain-computer interface performance by addressing limited training samples and improving classification accuracy.

Contribution

The study presents a novel cDCGAN approach for EEG data augmentation, demonstrating its effectiveness in improving BCI classification with limited data.

Findings

Artificial EEG data learned features similar to real data.

Generated data can match or surpass raw data in classification accuracy.

Data augmentation with cDCGAN improves BCI performance with small datasets.

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.