Decoding information from bio-signals such as EEG, using machine learning has been a
challenge due to the small data-sets and difficulty to obtain labels. We propose a
reconstruction-based self-supervised learning model, the masked auto-encoder for EEG
(MAEEG), for learning EEG representations by learning to reconstruct the masked EEG
features using a transformer architecture. We found that MAEEG can learn representations
that significantly improve sleep stage classification (~ 5% accuracy increase) when only a …