  Objective. Supervised learning paradigms are often limited by the amount of
labeled data that is available. This phenomenon is particularly problematic in
clinically-relevant data, such as electroencephalography (EEG), where labeling
can be costly in terms of specialized expertise and human processing time.
Consequently, deep learning architectures designed to learn on EEG data have
yielded relatively shallow models and performances at best similar to those of
traditional feature-based approaches. However, in most situations, unlabeled
data is available in abundance. By extracting information from this unlabeled
data, it might be possible to reach competitive performance with deep neural
networks despite limited access to labels. Approach. We investigated
self-supervised learning (SSL), a promising technique for discovering structure
in unlabeled data, to learn representations of EEG signals. Specifically, we
explored two tasks based on temporal context prediction as well as contrastive
predictive coding on two clinically-relevant problems: EEG-based sleep staging
and pathology detection. We conducted experiments on two large public datasets
with thousands of recordings and performed baseline comparisons with purely
supervised and hand-engineered approaches. Main results. Linear classifiers
trained on SSL-learned features consistently outperformed purely supervised
deep neural networks in low-labeled data regimes while reaching competitive
performance when all labels were available. Additionally, the embeddings
learned with each method revealed clear latent structures related to
physiological and clinical phenomena, such as age effects. Significance. We
demonstrate the benefit of self-supervised learning approaches on EEG data. Our
results suggest that SSL may pave the way to a wider use of deep learning
models on EEG data.
