Brain-computer interface (BCI) technology based on electroencephalography (EEG) signals is growing rapidly and attracting widespread attention. However, due to the EEG acquisition methods, the quality and quantity of EEG signals are not able to be guaranteed. To alleviate the problems caused by the lack of data, in this paper, we introduce the applications of EEG signals using generative adversarial networks (GANs) which have shown great performance in image data augmentation and other time series data and then discuss their performance.