Wearable EEG devices have emerged as a promising alternative to polysomnography (PSG). As affordable and scalable solutions, their widespread adoption results in the collection of massive volumes of unlabeled data that cannot be analyzed by clinicians at scale. Meanwhile, the recent success of deep learning for sleep scoring has relied on large annotated datasets. Self-supervised learning (SSL) offers an opportunity to bridge this gap, leveraging unlabeled signals to address label scarcity and reduce annotation effort. In this paper, we present the first systematic evaluation of SSL for sleep staging using wearable EEG. We introduce a structured benchmarking framework encompassing a range of SSL paradigms and propose a specialized pipeline tailored to the wearable EEG domain, evaluating them on two sleep databases acquired with the Ikon Sleep wearable headband: BOAS, a high-quality benchmark containing PSG and wearable EEG recordings with consensus labels, and HOGAR, a large collection of home-based, self-recorded, and unlabeled recordings. Three evaluation scenarios are defined to study label efficiency, representation quality, and cross-dataset generalization. Results show that SSL consistently improves classification performance by up to 10% over supervised baselines, with gains particularly evident when labeled data is scarce. SSL achieves clinical-grade accuracy above 80% leveraging only 5% to 10% of labeled data, while the supervised approach requires twice the labels. Additionally, the proposed domain-specific SSL pipeline outperforms the evaluated general-purpose EEG foundation models across all scenarios. Our findings demonstrate the potential of SSL to enable label-efficient sleep staging with wearable EEG, reducing reliance on manual annotations and advancing the development of affordable sleep monitoring systems.

翻译：可穿戴脑电设备已成为多导睡眠监测（PSG）的一种有前景的替代方案。作为经济且可扩展的解决方案，其广泛采用导致收集到海量无法由临床医生大规模分析的未标记数据。与此同时，深度学习在睡眠评分方面的近期成功依赖于大型标注数据集。自监督学习（SSL）为弥合这一差距提供了契机，它利用未标记信号来解决标签稀缺问题并减少标注工作量。本文首次系统评估了基于可穿戴脑电的睡眠分期自监督学习方法。我们引入了一个涵盖多种SSL范式的结构化基准测试框架，并提出了一个针对可穿戴脑电领域定制的专用处理流程，在两个使用Ikon Sleep可穿戴头带采集的睡眠数据库上进行评估：BOAS（一个包含具有共识标签的PSG和可穿戴脑电记录的高质量基准数据集）和HOGAR（一个大规模的家庭自采集未标记记录集）。我们定义了三种评估场景以研究标签效率、表示质量和跨数据集泛化能力。结果表明，SSL持续将分类性能较监督基线提升高达10%，在标签数据稀缺时增益尤为明显。SSL仅利用5%至10%的标签数据即可实现超过80%的临床级准确率，而监督方法则需要两倍的标签量。此外，所提出的领域专用SSL流程在所有评估场景中均优于已测试的通用脑电基础模型。我们的研究结果证明了SSL在实现可穿戴脑电标签高效睡眠分期方面的潜力，能够减少对人工标注的依赖，并推动经济型睡眠监测系统的发展。