A new data augmentation method for EEG features based on the hybrid model of broad-deep networks

Abstract

Decades after data augmentation was first proposed in brain-computer interface (BCI), the authenticity and performance still do not meet rational requirements, which is directly related to the fact that the augmentation methods do not provide real electroencephalograph (EEG) trials. Here we show how to generate a numerous authentic EEG from the original calibrated EEG by using a novel hybrid model of broad-deep networks, which eliminates the lack of authenticity of data generated by GAN and other methods. First, we design an EEG evoked experiment with a complex boundary avoidance task to collect the EEG of different subjects. This experiment can effectively highlight the differences of EEG features of different subjects that makes the results more reliable when using our novel hybrid model to measure the similarity. A new hybrid model of broad-deep networks is proposed to measure the similarity of different subjects in this study. And the EEG features of the two subjects with the highest similarity are combined to generate an augmented feature set. On the condition of satisfying the authenticity of EEG, the augmented feature set is significantly better than the original feature set in data dimension and quality. Finally, we verify the classification effect of the augmented feature set, and the results show that the proposed method can effectively generate real EEG data and improve the classification performance to a high reliability level for complex boundary avoidance tasks under limited EEG conditions. In addition, we observe the obvious advantages of this model over traditional deep learning methods in terms of training time and memory overhead.