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 classification. Our findings provide insight into how reconstruction-based SSL could help representation learning for EEG.
@article{arxiv.2211.02625,
title = {MAEEG: Masked Auto-encoder for EEG Representation Learning},
author = {Hsiang-Yun Sherry Chien and Hanlin Goh and Christopher M. Sandino and Joseph Y. Cheng},
journal= {arXiv preprint arXiv:2211.02625},
year = {2022}
}
Comments
10 pages, 5 figures, accepted by Workshop on Learning from Time Series for Health, NeurIPS2022 as poster presentation