CDBG：面向脑电负荷识别中拓扑与预测偏移的因果动机双不变性学习

中文摘要 AI 辅助

将基于脑电图（EEG）的脑力负荷识别泛化到未见受试者仍是一项艰巨挑战，原因在于严重的受试者间变异性。尽管功能性脑图能有效建模分布式认知动态，但其固有的受试者特异性会引发两种耦合的分布偏移：潜在功能连接中的类条件拓扑偏移，以及学习到的表示到标签映射中的预测机制偏移。受受试者引起的分布偏移启发，我们提出了CDBG，一个面向脑图的因果动机双不变性学习框架。CDBG通过两阶段理性学习流程来解耦并缓解这些偏移。首先，它采用随机边掩蔽来提取稀疏的、与负荷预测相关的图理性，并通过负荷条件拉普拉斯谱对齐进行正则化，以强制跨受试者的拓扑不变性。其次，它对图表示应用受试者级别的不变风险最小化（IRM），确保环境级别的风险平稳性。在自建的空中交通管制员脑电认知负荷数据集和多个公开数据集上，采用严格的留一受试者交叉验证协议进行的大量实验表明，CDBG显著优于最先进的跨受试者和基于图的基线，将Macro-F1分数最高提升了4.23%，同时提供了神经生理学上可解释的功能理性。

英文摘要

Generalizing Electroencephalography (EEG)-based mental workload recognition to unseen subjects remains a formidable challenge due to severe inter-subject variability. While functional brain graphs effectively model distributed cognitive dynamics, their inherent subject-specificity induces two coupled distribution shifts: a class-conditional topological shift in the underlying functional connectivity, and a predictive mechanism shift in the learned representation-to-label mapping. Motivated by the subject-induced distribution shifts, we propose CDBG, a Causally motivated Dual-invariance learning framework for Brain Graphs. CDBG disentangles and mitigates these shifts via a two-stage rationale learning pipeline. First, it employs stochastic edge masking to extract sparse, workload-predictive graph rationales, regularized by workload-conditional Laplacian spectral alignment to enforce topological invariance across subjects. Second, it applies subject-wise Invariant Risk Minimization (IRM) to the graph representations, ensuring environment-wise risk stationarity. Extensive experiments on a self-built air traffic controller EEG cognitive workload dataset and multiple public datasets under a strict leave-one-subject-out protocol demonstrate that CDBG significantly outperforms state-of-the-art cross-subject and graph-based baselines, improving the Macro-F1 score by up to 4.23%, while simultaneously providing neurophysiologically interpretable functional rationales.

发表机构

• Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
• Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
• National University of Singapore（新加坡国立大学）
• Xi’an Jiaotong University（西安交通大学）
• Chinese Academy of Sciences（中国科学院）

机构由 AI 辅助整理，请以论文原文为准。