  Recently, deep learning has shown to be effective for Electroencephalography
(EEG) decoding tasks. Yet, its performance can be negatively influenced by two
key factors: 1) the high variance and different types of corruption that are
inherent in the signal, 2) the EEG datasets are usually relatively small given
the acquisition cost, annotation cost and amount of effort needed. Data
augmentation approaches for alleviation of this problem have been empirically
studied, with augmentation operations on spatial domain, time domain or
frequency domain handcrafted based on expertise of domain knowledge. In this
work, we propose a principled approach to perform dynamic evolution on the data
for improvement of decoding robustness. The approach is based on
distributionally robust optimization and achieves robustness by optimizing on a
family of evolved data distributions instead of the single training data
distribution. We derived a general data evolution framework based on
Wasserstein gradient flow (WGF) and provides two different forms of evolution
within the framework. Intuitively, the evolution process helps the EEG decoder
to learn more robust and diverse features. It is worth mentioning that the
proposed approach can be readily integrated with other data augmentation
approaches for further improvements. We performed extensive experiments on the
proposed approach and tested its performance on different types of corrupted
EEG signals. The model significantly outperforms competitive baselines on
challenging decoding scenarios.
