Bagging Adversarial Neural Networks for Domain Adaptation in Non-Stationary EEG

A major issue in bringing real-world applications of machine learning outside the laboratory is the difference in the data distributions between training and testing stages or domains. The diverging statistical properties in different domains can lead to decay the prediction performance. The technical term for a change in the distribution of features is covariate shift, which also happens to be a common challenge in electroencephalogram (EEG) based brain-computer interface (BCI); this is due to the presence of non-stationarities in the EEG signals. It is also the case that collecting and labelling samples is expensive, resulting in small datasets that are not in tune with the "big data" spirit that is the characteristic of the era. In this paper, we introduce a new method that handles domain adaptation in small datasets; the method combines elements of unsupervised domain adaptation with ensemble methods. We evaluate on real-world datasets corresponding to motor-imagery detection (BCI competition 2008 dataset 2A). The method produces state of the art results.