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.

翻译：分析脑电图（EEG）时间序列具有挑战性，尤其是在深度神经网络应用中，这源于人类受试者间的高度变异性以及通常较小的数据集规模。为应对这些挑战，已有研究者提出自监督学习等多种策略，但这些方法通常依赖于大规模经验数据集。受计算机视觉领域最新进展的启发，我们提出一种名为“频率预训练”的预训练任务，通过预测随机生成合成时间序列的频率内容来预训练睡眠分期神经网络。实验表明，在数据有限且受试者较少的场景下，我们的方法超越了全监督学习，在受试者众多的场景中也能达到同等性能。此外，研究结果既凸显了频率信息对睡眠分期评分的重要性，也证实深度神经网络能利用频率以外的信息提升睡眠分期性能——这与前人研究结论一致。我们预期该方法将广泛应用于脑电图数据有限或仅来源于少量受试者的场景，包括脑机接口领域。