Understanding how local neurophysiological patterns interact with global brain dynamics is essential for decoding human emotions from EEG signals. However, existing deep learning approaches often overlook the brain's intrinsic spatial organization, failing to simultaneously capture local topological relations and global dependencies. To address these challenges, we propose Neuro-HGLN, a Neurologically-informed Hierarchical Graph-Transformer Learning Network that integrates biologically grounded priors with hierarchical representation learning. Neuro-HGLN first constructs a spatial Euclidean prior graph based on physical electrode distances to serve as an anatomically grounded inductive bias. A learnable global dynamic graph is then introduced to model functional connectivity across the entire brain. In parallel, to capture fine-grained regional dependencies, Neuro-HGLN builds region-level local graphs using a multi-head self-attention mechanism. These graphs are processed synchronously through local-constrained parallel GCN layers to produce region-specific representations. Subsequently, an iTransformer encoder aggregates these features to capture cross-region dependencies under a dimension-as-token formulation. Extensive experiments demonstrate that Neuro-HGLN achieves state-of-the-art performance on multiple benchmarks, providing enhanced interpretability grounded in neurophysiological structure. These results highlight the efficacy of unifying local topological learning with cross-region dependency modeling for robust EEG emotion recognition.

翻译：理解局部神经生理模式如何与全局大脑动力学相互作用，对于从脑电信号解码人类情绪至关重要。然而，现有的深度学习方法常常忽视大脑固有的空间组织，未能同时捕捉局部拓扑关系和全局依赖关系。为应对这些挑战，我们提出了Neuro-HGLN，一种融合生物学先验知识与分层表征学习的神经学启发的分层图-Transformer学习网络。Neuro-HGLN首先基于物理电极距离构建空间欧几里得先验图，作为解剖学基础的归纳偏置。随后引入一个可学习的全局动态图，以建模整个大脑的功能连接。同时，为捕捉细粒度的区域依赖关系，Neuro-HGLN使用多头自注意力机制构建区域级局部图。这些图通过局部约束的并行图卷积网络层同步处理，生成区域特异性表征。随后，一个iTransformer编码器在维度即令牌的框架下聚合这些特征，以捕捉跨区域依赖关系。大量实验表明，Neuro-HGLN在多个基准测试中实现了最先进的性能，并提供了基于神经生理结构的增强可解释性。这些结果凸显了将局部拓扑学习与跨区域依赖建模相统一对于鲁棒的脑电情绪识别的有效性。