GRFBrain：用于脑电动态建模的图结构整流流
GRFBrain: Graph-Structured Rectified Flows for EEG Dynamic Modeling
- Hokkaido University（北海道大学）
- The University of Osaka（大阪大学）
- University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
- PediaMed AI
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出GRFBrain框架，将条件均值预测与随机残差传输分离，利用图高斯源和条件速度场进行脑电动态建模，并明确区分有用残差传输与其他改进来源。
AI中文摘要：
从脑电图（EEG）预测时变功能连接需要同时建模历史依赖趋势和跨通道的结构化变异性。条件流匹配为分布预测提供了框架，但图信息源分布相对于各向同性噪声和强确定性预测器是否具有实际优势仍不清楚。我们提出了一种图结构残差流框架，将条件均值预测与随机残差传输分离。仅基于历史的预测器估计未来连接图，而图高斯源通过基于拉普拉斯的协方差编码源自过去连接性的依赖关系。条件速度场将源样本传输到未来图残差，传输时间与物理EEG时间明确区分。我们的研究确定了区分有用残差传输与可归因于确定性预测、学习表示和采样效应的改进所需的条件和控制。
英文摘要：
Forecasting time-varying functional connectivity from electroencephalography (EEG) requires modeling both history-dependent trends and structured variability across channels. Conditional flow matching provides a framework for distributional forecasting, yet it remains unclear whether graph-informed source distributions offer practical advantages over isotropic noise and strong deterministic predictors. We introduce a graph-structured residual flow framework that separates conditional mean prediction from stochastic residual transport. A history-only predictor estimates the future connectivity graph, while a graph Gaussian source encodes dependencies derived from past connectivity through a Laplacian-based covariance. A conditional velocity field transports source samples to future graph residuals, with transport time explicitly distinguished from physical EEG time. Our study identifies the conditions and controls needed to distinguish useful residual transport from improvements attributable to deterministic prediction, learned representations, and sampling effects.
