STDA-Net achieves 89.03% average accuracy and 87.64% macro F1 in cross-dataset sleep staging by processing 2D spectrograms with temporal modeling and unsupervised adversarial alignment, outperforming 1D baselines with lower variance.
Analysis of a sleep-dependent neuronal feedback loop: The slow-wave microcontinuity of the eeg
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STDA-Net: Spectrogram-Based Domain Adaptation for cross-dataset Sleep Stage Classification
STDA-Net achieves 89.03% average accuracy and 87.64% macro F1 in cross-dataset sleep staging by processing 2D spectrograms with temporal modeling and unsupervised adversarial alignment, outperforming 1D baselines with lower variance.