Deep learning for EEG-based sleep stage classification: a review.

distribution analysis indicate that the performance of existing models is highly dependent on the dataset, and that the evaluation system overly relies on overall accuracy. As a result, it fails to reveal performance weaknesses on minority stages and pathological data. Furthermore, this review discusses the core challenges current models face in generalization, interpretability, and clinical applicability. To address these challenges, the construction of diverse datasets, optimization of architecture design, enhancement of interpretability mechanisms, and promotion of clinical validation are also proposed as future directions. This review aims to guide the transition from algorithmic innovation to clinically reliable sleep staging research.