Data augmentation for learning predictive models on EEG: a systematic comparison

Objective. The use of deep learning for electroencephalography (EEG) classification tasks has been rapidly growing in the last years, yet its application has been limited by the relatively small size of EEG datasets. Data augmentation, which consists in artificially increasing the size of the dataset during training, can be employed to alleviate this problem. While a few augmentation transformations for EEG data have been proposed in the literature, their positive impact on performance is often evaluated on a single dataset and compared to one or two competing augmentation methods. This work proposes to better validate the existing data augmentation approaches through a unified and exhaustive analysis. Approach. We compare quantitatively 13 different augmentations with two different predictive tasks, datasets and models, using three different types of experiments. Main results. We demonstrate that employing the adequate data augmentations can bring up to 45% accuracy improvements in low data regimes compared to the same model trained without any augmentation. Our experiments also show that there is no single best augmentation strategy, as the good augmentations differ on each task. Significance. Our results highlight the best data augmentations to consider for sleep stage classification and motor imagery brain-computer interfaces. More broadly, it demonstrates that EEG classification tasks benefit from adequate data augmentation.

Automated summary

This study validates existing EEG data augmentation approaches through a unified and exhaustive analysis. The results demonstrate that employing appropriate data augmentations can significantly improve the accuracy of EEG classification tasks in low data regimes. Furthermore, the study shows that there is no single best augmentation strategy for different tasks.