# Domain-Specific Denoising Diffusion Probabilistic Models for Brain Dynamics

arXiv: https://arxiv.org/abs/2305.04200 · HTML: https://mlsanity.com/paper/2305.04200/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2023.

## Built on by (most cited first, 4 of 4)

- [A Survey on Diffusion Models for Time Series and Spatio-Temporal Data](https://mlsanity.com/paper/2404.18886/) (2024)
- [On the Design Fundamentals of Diffusion Models: A Survey](https://mlsanity.com/paper/2306.04542/) (2023)
- [A Comprehensive Survey on Generative Diffusion Models for Structured Data](https://mlsanity.com/paper/2306.04139/) (2023)
- [NeuroTTT: Bridging Pretraining-Downstream Task Misalignment in EEG Foundation Models via Test-Time Training](https://mlsanity.com/paper/2509.26301/) (2025)

## Abstract (arXiv)

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.