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EEG signals are usually simple to obtain but expensive to label. Althoughsupervised 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 computervision (CV) and natural language processing (NLP), can employ unlabeled data tomake up for the data shortage of supervised learning. In this paper, we proposea self-supervised contrastive learning method of EEG signals for sleep stageclassification. During the training process, we set up a pretext task for thenetwork in order to match the right transformation pairs generated from EEGsignals. In this way, the network improves the representation ability bylearning the general features of EEG signals. The robustness of the networkalso gets improved in dealing with diverse data, that is, extracting constantfeatures from changing data. In detail, the network's performance depends onthe choice of transformations and the amount of unlabeled data used in thetraining process of self-supervised learning. Empirical evaluations on theSleep-edf dataset demonstrate the competitive performance of our method onsleep staging (88.16% accuracy and 81.96% F1 score) and verify theeffectiveness of SSL strategy for EEG signal analysis in limited labeled dataregimes. All codes are provided publicly online.

Self-supervised Contrastive Learning for EEG-based Sleep Staging