  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.
