Electroencephalogram (EEG) signals are vital for automated seizure detection, but their inherent noise makes robust representation learning challenging. Existing graph construction methods, whether correlation-based or learning-based, often generate redundant or irrelevant edges due to the noisy nature of EEG data. This significantly impairs the quality of graph representation and limits downstream task performance. Motivated by the remarkable reasoning and contextual understanding capabilities of large language models (LLMs), we explore the idea of using LLMs as graph edge refiners. Specifically, we propose a two-stage framework: we first verify that LLM-based edge refinement can effectively identify and remove redundant connections, leading to significant improvements in seizure detection accuracy and more meaningful graph structures. Building on this insight, we further develop a robust solution where the initial graph is constructed using a Transformer-based edge predictor and multilayer perceptron, assigning probability scores to potential edges and applying a threshold to determine their existence. The LLM then acts as an edge set refiner, making informed decisions based on both textual and statistical features of node pairs to validate the remaining connections. Extensive experiments on TUSZ dataset demonstrate that our LLM-refined graph learning framework not only enhances task performance but also yields cleaner and more interpretable graph representations.

翻译：脑电图信号对自动癫痫检测至关重要，但其固有的噪声特性使得稳健的表示学习充满挑战。现有的图构建方法——无论是基于相关性还是基于学习——常因脑电图数据的噪声特性而生成冗余或无关的边。这严重损害了图表示的质量，限制了下游任务的性能。受大语言模型卓越的推理能力和上下文理解能力的启发，我们探索将LLM用作图边精炼器的思路。具体而言，我们提出一个两阶段框架：首先验证基于LLM的边精炼能够有效识别并移除冗余连接，从而显著提升癫痫检测精度并生成更具意义的图结构。基于这一发现，我们进一步开发了一个稳健的解决方案：初始图通过Transformer边预测器与多层感知机构建，为潜在边分配概率分数，并设定阈值判定其存在性。随后，LLM作为边集精炼器，基于节点对的文本特征与统计特征做出明智决策，验证剩余连接的有效性。在TUSZ数据集上的大量实验表明，经LLM精炼的图学习框架不仅提升了任务性能，还生成了更清晰、更具可解释性的图表示。