MAEEG: Masked Auto-encoder for EEG Representation Learning
AuthorsHsiang-Yun Sherry Chien, Hanlin Goh, Christopher M. Sandino, Joseph Y. Cheng
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 increase) when only a small number of labels are given. We also found that input sample lengths and different ways of masking during reconstruction-based SSL pretraining have a huge effect on downstream model performance. Specifically, learning to reconstruct a larger proportion and more concentrated masked signal results in better performance on sleep classifica- tion. Our findings provide insight into how reconstruction-based SSL could help representation learning for EEG.