  The past few years have witnessed a remarkable advance in deep learning for
EEG-based sleep stage classification (SSC). However, the success of these
models is attributed to possessing a massive amount of labeled data for
training, limiting their applicability in real-world scenarios. In such
scenarios, sleep labs can generate a massive amount of data, but labeling these
data can be expensive and time-consuming. Recently, the self-supervised
learning (SSL) paradigm has shined as one of the most successful techniques to
overcome the scarcity of labeled data. In this paper, we evaluate the efficacy
of SSL to boost the performance of existing SSC models in the few-labels
regime. We conduct a thorough study on three SSC datasets, and we find that
fine-tuning the pretrained SSC models with only 5% of labeled data can achieve
competitive performance to the supervised training with full labels. Moreover,
self-supervised pretraining helps SSC models to be more robust to data
imbalance and domain shift problems. The code is publicly available at
https://github.com/emadeldeen24/eval_ssl_ssc.
