Self-supervised learning addresses the challenge encountered by many
supervised methods, i.e. the requirement of large amounts of annotated data.
This challenge is particularly pronounced in fields such as the
electroencephalography (EEG) research domain. Self-supervised learning operates
instead by utilizing pseudo-labels, which are generated by pretext tasks, to
obtain a rich and meaningful data representation. In this study, we aim at
introducing a dual-stream pretext task architecture that operates both in the
time and frequency domains. In particular, we have examined the incorporation
of the novel Frequency Similarity (FS) pretext task into two existing pretext
tasks, Relative Positioning (RP) and Temporal Shuffling (TS). We assess the
accuracy of these models using the Physionet Challenge 2018 (PC18) dataset in
the context of the downstream task sleep stage classification. The inclusion of
FS resulted in a notable improvement in downstream task accuracy, with a 1.28
percent improvement on RP and a 2.02 percent improvement on TS. Furthermore,
when visualizing the learned embeddings using Uniform Manifold Approximation
and Projection (UMAP), distinct clusters emerge, indicating that the learned
representations carry meaningful information.

利用自我监督学习解决了许多有监督方法所面临的大量注释数据的需求，本研究在时域和频域中引入了双流预训练任务架构，并在睡眠阶段分类任务中通过引入频率相似性预训练任务，成功地提高了任务精度，并得到了具有意义信息的学习嵌入表示。