Frequency‐Enhanced Single‐Channel EEG Sleep Staging via Cross‐Frequency Interaction

Automatic sleep staging using single‐channel electroencephalography (EEG) is important for reducing the acquisition complexity of sleep monitoring and may support future wearable and home‐based applications. Although recent deep learning models have demonstrated promising performance, they usually prioritize time waveform representations or frequency domain features in isolation, neglecting the synergy between these two domains. Moreover, the intrinsic cross‐frequency interactions in sleep EEG signals are rarely modelled explicitly in existing studies. To address these limitations, we propose a frequency‐enhanced single‐channel sleep staging framework via cross‐frequency interaction. The proposed model simultaneously learns temporal‐domain representations from raw EEG signals and frequency‐domain representations derived from the short‐time Fourier transform (STFT). To better capture oscillatory dynamics in sleep EEG, amplitude and phase components are explicitly decoupled and independently projected to construct amplitude and phase tokens. A cross‐attention mechanism inspired by phase–amplitude coupling (PAC) is introduced to explicitly capture cross‐frequency interactions, in which phase information modulates amplitude responses within the frequency domain. Finally, temporal and spectral features are fused and further modelled using sequence learning to capture sleep stage transitions across successive epochs. Experiments conducted on two public datasets, Sleep‐EDF and MASS‐SS3, demonstrate the effectiveness of the proposed approach. The model achieves 87.0% accuracy and 82.0% macro F1‐score on Sleep‐EDF, and 87.8% accuracy and 83.1% macro F1‐score on MASS‐SS3. These results indicate that the proposed dual‐domain and cross‐frequency interaction modelling framework can effectively improve the discriminative capability of single‐channel sleep staging models.