The differences in brain dynamics across human subjects, commonly referred to as human artifacts, have long been a challenge in the field, severely limiting the generalizability of brain dynamics recognition models. Traditional methods for human artifact removal typically employ spectrum filtering or blind source separation, based on simple prior distribution assumptions, which ultimately constrain the capacity to model each subject's domain variance. In this paper, we propose a novel approach to model human artifact removal as a generative denoising process, capable of simultaneously generating and learning subject-specific domain variance and invariant brain signals. We introduce the Domain Specific Denoising Diffusion Probabilistic Model (DS-DDPM), which decomposes the denoising process into subject domain variance and invariant content at each step. By incorporating subtle constraints and probabilistic design, we formulate domain variance and invariant content into orthogonal spaces and further supervise the domain variance with a subject classifier. This method is the first to explicitly separate human subject-specific variance through generative denoising processes, outperforming previous methods in two aspects: 1) DS-DDPM can learn more accurate subject-specific domain variance through domain generative learning compared to traditional filtering methods, and 2) DS-DDPM is the first approach capable of explicitly generating subject noise distribution. Comprehensive experimental results indicate that DS-DDPM effectively alleviates domain distribution bias for cross-domain brain dynamics signal recognition.

翻译：人类受试者之间的脑动力学差异（通常称为人为伪迹）一直是该领域的长期挑战，严重限制了脑动力学识别模型的泛化能力。传统的人为伪迹去除方法通常基于简单的先验分布假设，采用频谱滤波或盲源分离技术，最终限制了对每个受试者领域差异的建模能力。本文提出一种将人为伪迹去除建模为生成式去噪过程的新方法，能够同时生成并学习受试者特定的领域差异与不变脑信号。我们引入领域特定去噪扩散概率模型（DS-DDPM），该模型在每一步将去噪过程分解为受试者领域差异与不变内容。通过加入精细约束与概率设计，我们将领域差异与不变内容映射到正交空间，并进一步利用受试者分类器对领域差异进行监督。该方法首次通过生成式去噪过程显式分离人类受试者特定差异，在两方面优于先前方法：1) 与传统滤波方法相比，DS-DDPM通过领域生成学习可获取更精确的受试者特定领域差异；2) DS-DDPM是首个能够显式生成受试者噪声分布的方法。综合实验结果表明，DS-DDPM有效缓解了跨域脑动力学信号识别中的领域分布偏差。