Analyzing electroencephalographic (EEG) time series can be challenging,
especially with deep neural networks, due to the large variability among human
subjects and often small datasets. To address these challenges, various
strategies, such as self-supervised learning, have been suggested, but they
typically rely on extensive empirical datasets. Inspired by recent advances in
computer vision, we propose a pretraining task termed "frequency pretraining"
to pretrain a neural network for sleep staging by predicting the frequency
content of randomly generated synthetic time series. Our experiments
demonstrate that our method surpasses fully supervised learning in scenarios
with limited data and few subjects, and matches its performance in regimes with
many subjects. Furthermore, our results underline the relevance of frequency
information for sleep stage scoring, while also demonstrating that deep neural
networks utilize information beyond frequencies to enhance sleep staging
performance, which is consistent with previous research. We anticipate that our
approach will be advantageous across a broad spectrum of applications where EEG
data is limited or derived from a small number of subjects, including the
domain of brain-computer interfaces.

通过预测随机生成的合成时间序列的频率内容的一种预训练任务，我们的实验表明，我们的方法在数据有限和受试者少的情况下超越完全监督学习，在有很多受试者的情况下与其性能相匹配。此外，我们的结果强调了频率信息对于睡眠阶段判定的相关性，并且还表明，深度神经网络利用除频率以外的信息来提高睡眠分期的准确性，这与以前的研究一致。我们预计我们的方法将在脑 - 机接口等 EEG 数据有限或来源于少数受试者的广泛应用领域中具有优势。