Title: 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.