  EEG signals are usually simple to obtain but expensive to label. Although
supervised learning has been widely used in the field of EEG signal analysis,
its generalization performance is limited by the amount of annotated data.
Self-supervised learning (SSL), as a popular learning paradigm in computer
vision (CV) and natural language processing (NLP), can employ unlabeled data to
make up for the data shortage of supervised learning. In this paper, we propose
a self-supervised contrastive learning method of EEG signals for sleep stage
classification. During the training process, we set up a pretext task for the
network in order to match the right transformation pairs generated from EEG
signals. In this way, the network improves the representation ability by
learning the general features of EEG signals. The robustness of the network
also gets improved in dealing with diverse data, that is, extracting constant
features from changing data. In detail, the network's performance depends on
the choice of transformations and the amount of unlabeled data used in the
training process of self-supervised learning. Empirical evaluations on the
Sleep-edf dataset demonstrate the competitive performance of our method on
sleep staging (88.16% accuracy and 81.96% F1 score) and verify the
effectiveness of SSL strategy for EEG signal analysis in limited labeled data
regimes. All codes are provided publicly online.
