MAEEG: Masked Auto-encoder for EEG Representation Learning

This paper was accepted at the Workshop on Learning from Time Series for Health at NeurIPS 2022.
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…