Proceedings of the Annual Meeting of the Cognitive Science Society

The development of brain–computer interfaces (BCI) has provided a solid data foundation for sleep staging. Transformer-based methods have achieved moderate sleep staging by capturing temporal dependencies within time-frequency representations. However, Transformers with substantial computational overhead are limited to capturing pairwise temporal dependencies rather than group-wise temporal dependencies. To address these issues, we propose a Dual Evolving Graph Network (DEGSleepNet) for sleep staging. DEGSleepNet consists of multiple Mamba with one-dimension inverse discrete cosine transform (MambaIDCT) blocks and dual evolving graph (DEvoGraph) blocks. DEvoGraph consists of a time EvoGraph and a frequency EvoGraph. Time EvoGraph sequentially captures both group-wise and pairwise temporal dependencies, while frequency EvoGraph does the same for frequency dependencies. Extensive experiments demonstrate DEGSleepNet achieves the best performance on public sleep datasets. Notably, DEGSleepNet has few model parameters, making it suitable for sleep monitoring on wearable devices.