EEGDTF: Time-Frequency Disentangled Diffusion for High-Fidelity EEG Signal Generation.

Abstract

EEG signal generation is hindered by challenges such as complex time-frequency structures, the lack of explicit spectral modeling, limited data availability, and limited generalization across subjects and tasks. To address these issues, we propose EEGDTF, a diffusion-based generative framework for synthesizing high-fidelity EEG signals with improved time-frequency modeling. EEGDTF first employs a multi-scale residual encoder to enhance temporal representation learning and training stability. It further introduces a dual-branch encoder-decoder architecture for time-frequency disentanglement: the frequency branch models both periodic and aperiodic components via power spectral parameterization, while the temporal branch captures waveform continuity and long-range dependencies. A frequency-guided cross-attention mechanism integrates both branches effectively. The model is optimized through a joint waveform and spectral loss, enabling stable clean-signal estimation during reverse sampling. Experiments on four benchmark datasets demonstrate that EEGDTF achieves state-of-the-art performance in both time and frequency domains, particularly under cross-subject conditions. These results underscore the model's robustness and generalizability, positioning EEGDTF as a reliable tool for EEG data augmentation and BCI-related applications.