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

Chenyu Hu, Siyue Liang, Rui Li

IEEE Journal of Biomedical and Health Informatics

|May 19, 2026

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This study introduces EEGDTF, a novel diffusion model for generating high-quality electroencephalogram (EEG) signals. EEGDTF enhances time-frequency modeling and improves generalization across subjects for EEG data augmentation.

Area of Science:

Neuroscience
Signal Processing
Artificial Intelligence

Background:

Electroencephalogram (EEG) signal generation faces challenges including complex time-frequency structures, limited data, and poor generalization.
Existing methods lack explicit spectral modeling and robust cross-subject performance.

Purpose of the Study:

To develop a diffusion-based generative framework, EEGDTF, for synthesizing high-fidelity EEG signals.
To improve time-frequency modeling and generalization capabilities of EEG signal generation.

Main Methods:

Proposed EEGDTF framework utilizing a multi-scale residual encoder for temporal representation.
Implemented a dual-branch encoder-decoder architecture for time-frequency disentanglement.
Employed a frequency-guided cross-attention mechanism and joint waveform/spectral loss for optimization.

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Main Results:

EEGDTF achieved state-of-the-art performance in both time and frequency domains across four benchmark datasets.
Demonstrated superior robustness and generalizability, especially under cross-subject conditions.
Successfully synthesized high-fidelity EEG signals with improved time-frequency characteristics.

Conclusions:

EEGDTF offers a reliable solution for EEG data augmentation, addressing limitations of existing generative models.
The framework's robust performance positions it as a valuable tool for Brain-Computer Interface (BCI) applications.
Highlights the potential of diffusion models in advancing EEG signal synthesis and analysis.