STDA-Net: Spectrogram-Based Domain Adaptation for cross-dataset Sleep Stage Classification

ArXi:2605.06736v1 Announce Type: cross Accurate sleep stage classification across datasets remains challenging due to variability in EEG channel montages, sampling rates, recording environments, and subject populations. Although deep learning has shown considerable promise for automated sleep staging, most existing cross-dataset methods rely on one-dimensional EEG signal representations, whereas the use of two-dimensional spectrogram-based inputs within an unsupervised domain adaptation framework has remained largely unexplored.