2024/03/13 by Niklas Grieger, Grieger, Niklas, Siamak Mehrkanoon +3
Computer Science · Psychology · #Computer science #FOS: Biological sciences #FOS: Computer and information sciences #Geology #Machine Learning (cs.LG) #Machine learning #Programming language #Quantitative Methods (q-bio.QM) #Series (stratigraphy) #Sleep (system call) #Sleep and Work-Related Fatigue #Time Series Analysis and Forecasting #Time series
paper · pdf · doi:10.48550/arxiv.2403.08592
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.