EEGDfus: A Conditional Diffusion Model for Fine-Grained EEG Denoising

Electroencephalogram (EEG) signals are vital in understanding brain activity, but their weak amplitude makes them susceptible to various artifacts. Accurate denoising of EEG data is crucial as a preprocessing step to ensure precise analysis and interpretation. In recent years, the diffusion model has garnered significant attention as a promising approach in generative modeling. This model effectively addresses the issue of over-smoothing in existing deep learning methods and thus has the potential to generate more refined denoised EEG signals. However, the generation process of the standard diffusion model is highly random, limiting its direct application to EEG denoising tasks. To address this limitation, we propose a conditional diffusion model specifically designed for EEG denoising. In this model, the standard diffusion model's denoising network is replaced by a novel dual-branch network, where noisy EEG information is used as a condition to guide the generation of corresponding clean EEG signals. This dual-branch structure leverages the complementary strengths of convolutional neural network (CNN) and Transformer architectures, integrating multi-scale features to comprehensively extract information from the signal. Extensive experiments demonstrate the remarkable performance of EEGDfus in EEG denoising. We tested it on two public datasets. Testing on two public datasets, EEGdenoiseNet and SSED, demonstrated that after denoising, the average correlation coefficient increased to 0.983 and 0.992 for EOG artifact removal, respectively. The proposed model outperforms commonly used baseline models, setting a new state-of-the-art benchmark in the field of EEG denoising.