发表机构
University of Central Florida（中佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究基于EEG的诊断问题，提出空间多专家图变换器，将EEG记录建模为动态功能连接图序列，用wPLI估计连接性，经层次图编码和多专家架构实现亚型感知推理，实验证明该方法在异常检测上性能良好且具可解释性。
AI 中文摘要
脑电图（EEG）异常源于跨时空尺度的神经同步动态变化，但许多计算方法将这些动态变化简化为静态特征。我们提出了一种空间多专家图变换器，将每个EEG记录建模为动态功能连接图序列。使用加权相位滞后指数（wPLI）估计时间分辨连接性，并通过层次图编码聚合从电极到区域和全局水平的信息。多专家变换器架构实现亚型感知推理，通过门控机制自适应融合专家输出以进行全局异常预测。在TUAB数据集上的实验显示了具有竞争力的异常EEG检测性能，并证明了动态图建模与自适应专家融合用于可解释的、亚型感知的时空分析的潜力。
英文摘要
Electroencephalographic (EEG) abnormalities arise from dynamic changes in neural synchrony across spatial and temporal scales, yet many computational approaches reduce these dynamics to static features. We present a Spatial Multi-Expert Graph Transformer that models each EEG recording as a sequence of dynamic functional connectivity graphs. Time-resolved connectivity is estimated using the weighted Phase Lag Index (wPLI), and hierarchical graph encoding aggregates information from electrode to regional and global levels. A multi-expert transformer architecture enables subtype-aware reasoning, with a gating mechanism adaptively fusing expert outputs for global abnormality prediction. Experiments on the TUAB dataset show competitive abnormal EEG detection performance and demonstrate the potential of dynamic graph modeling with adaptive expert fusion for interpretable, subtype-aware spatial--temporal analysis.
