脑电图 (EEG) 信号对于自动癫痫检测, 至关重要，但其固有的噪声使得稳健的表示学习具有挑战性。现有的图构建方法,，无论是基于相关性还是基于学习,，由于脑电图数据的噪声性质，通常会生成冗余或不相关的边缘。这显着损害了图形表示的质量并限制了下游任务的性能。受到大型语言模型 (LLMs), 卓越的推理和上下文理解能力的激励，我们探索了使用 LLM 作为图边缘细化器的想法。具体来说,我们提出了一个两阶段框架:我们首先验证基于LLM的边缘细化可以有效地识别和删除冗余连接,从而显着提高癫痫检测的准确性和更有意义的图形结构。基于这一见解,，我们进一步开发了一个强大的解决方案，其中使用基于 Transformer 的边缘预测器和多层感知器, 构建初始图，为潜在边缘分配概率分数并应用阈值来确定它们的存在。然后，LLM 充当边缘集细化器,，根据节点对的文本和统计特征做出明智的决策，以验证剩余的连接。对 TUSZ 数据集的大量实验表明，我们的 LLM 改进的图形学习框架不仅提高了任务性能，而且还产生了更清晰、更可解释的图形表示。
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.
科目:人工智能(cs.AI)
Subjects: Artificial Intelligence (cs.AI)