ATGAN: attention-based temporal GAN for EEG data augmentation in personal identification

With the development of brain–computer interfaces in recent years, deep learning models have been widely used in EEG-based personal identification. The training of complex models requires a large amount of data, but the acquisition of EEG signals is very time-consuming and energy-consuming. EEG data augmentation can reduce the overhead of EEG data acquisition and provide the data volume required by various complex models, which is conducive to applying EEG identification in practice. The generative adversarial network (GAN) has been applied to augment EEG data features. But the augmentation of temporal EEG signals is less studied. Therefore, this paper proposes a method of temporal EEG data augmentation based on an attention mechanism time series generative adversarial network (ATGAN), which can expand the original EEG dataset to facilitate the training of downstream deep learning classifiers. The experiment is conducted on the BCI Competition IV 2a dataset. The ATGAN model is trained with the temporal EEG signals after preprocessing, and the generated augmentation data by ATGAN is used to expand the original dataset. The accuracy of the EEG-based identification task on the augmented dataset is 7.78% higher than that on the original dataset, which proves the effectiveness of the proposed method.