CrossSleep: Multi-Scale Attention with Cross-Time Learning for Single Channel EEG-Based Sleep Staging

Abstract

Accurate sleep staging is essential for diagnosing sleep disorders and improving sleep health. While traditional methods rely on multichannel electroencephalogram (EEG) signals, single-channel EEG offers a more practical and non-intrusive alternative. However, the complexity of sleep dynamics across varying time scales presents a significant challenge for single-channel EEG-based staging. To address this, we propose a novel method combining Multi-Scale Attention and Cross-Time Learning to capture both local and global temporal dependencies in EEG signals. The Multi-Scale Attention Module extracts features at multiple temporal scales, while the Cross-Time Learning mechanism models long-term dependencies across time segments, improving classification performance. Our approach is evaluated on benchmark EEG sleep datasets, demonstrating superior accuracy and F1-scores, particularly in transition stages, compared to existing single-channel methods. The results suggest our method’s potential for real-world applications in portable and home-based sleep monitoring systems.