  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
