D4PM用双分支扩散模型联合去噪，提升脑电图多类伪影去除效果。
D4PM: A Dual-branch Driven Denoising Diffusion Probabilistic Model with Joint Posterior Diffusion Sampling for EEG Artifacts Removal
- 双分支结构隐式建模干净脑电与伪影分布
- 联合后验采样实现高保真脑电重建
- 适用于多类伪影、单通道数据，适合临床脑电信号处理
伪影去除对准确分析和解读脑电图（EEG）信号至关重要。传统方法在强伪影-脑电相关性或单通道数据下表现不佳。基于扩散的生成模型近年展现出强大潜力，显著提升细粒度噪声抑制并减少过平滑。但现有方法存在两大局限：缺乏时序建模影响可解释性，且采用单伪影训练范式忽略了伪影间的差异。为此，我们提出D4PM，一种双分支驱动的去噪扩散概率模型，统一实现多类型伪影去除。引入双分支条件扩散架构，隐式建模干净脑电与伪影的数据分布；进一步设计联合后验采样策略，协同整合互补先验以实现高保真脑电重构。在两个公开数据集上的大量实验表明，D4PM表现优异，在眼动伪影（EOG）去除任务中达到新最佳性能，超越所有公开基线方法。代码已开源：https://github.com/flysnow1024/D4PM。
原文摘要 · Abstract (English)
Artifact removal is critical for accurate analysis and interpretation of Electroencephalogram (EEG) signals. Traditional methods perform poorly with strong artifact-EEG correlations or single-channel data. Recent advances in diffusion-based generative models have demonstrated strong potential for EEG denoising, notably improving fine-grained noise suppression and reducing over-smoothing. However, existing methods face two main limitations: lack of temporal modeling limits interpretability and the use of single-artifact training paradigms ignore inter-artifact differences. To address these issues, we propose D4PM, a dual-branch driven denoising diffusion probabilistic model that unifies multi-type artifact removal. We introduce a dual-branch conditional diffusion architecture to implicitly model the data distribution of clean EEG and artifacts. A joint posterior sampling strategy is further designed to collaboratively integrate complementary priors for high-fidelity EEG reconstruction. Extensive experiments on two public datasets show that D4PM delivers superior denoising. It achieves new state-of-the-art performance in EOG artifact removal, outperforming all publicly available baselines. The code is available at https://github.com/flysnow1024/D4PM.
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