Proceedings of the Annual Meeting of the Cognitive Science Society

Accurate sleep staging plays a crucial role in diagnosing patients' sleep health. Numerous studies have confirmed that data from different views can highlight distinct characteristics. Based on the time-domain (TD) EEG, we constructed two additional views: the Power Spectral Density (PSD) and the time-frequency representation (TFR). Notably, there is no universally applicable model suitable for all types of data. That is to say, the characteristics of data should align with the model structure. Therefore, we designed specialized expert models for learning different views. However, directly combining features extracted from different views often results in excessive redundancy, especially for different views of the same data which the underlying data remains essentially identical. To address these issues, we proposed a multi-view mixture-of-experts (MoESleepNet) model for sleep staging, which achieves the best performance on SleepEDF20, SleepEDF78 and SHHS single-channel datasets. This study provides valuable ideas for multimodal signal classification.