In terms of seizure prediction, how to fully mine relational data information among multiple channels of epileptic EEG? This is a scientific research subject worthy of further exploration. Recently, we propose a multi-dimensional enhanced seizure prediction framework, which mainly includes information reconstruction space, graph state encoder, and space-time predictor. It takes multi-channel spatial relationship as breakthrough point. At the same time, it reconstructs data unit from frequency band level, updates graph coding representation, and explores space-time relationship. Through experiments on CHB-MIT dataset, sensitivity of the model reaches 98.61%, which proves effectiveness of the proposed model.

中文翻译：

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基于图卷积网络的多维增强癫痫预测框架。

在癫痫预测方面，如何充分挖掘癫痫脑电多通道之间的关联数据信息？这是一个值得进一步探索的科学研究课题。最近，我们提出了一个多维增强的癫痫预测框架，主要包括信息重构空间、图状态编码器和时空预测器。它以多通道空间关系为切入点。同时从频段层面重构数据单元，更新图编码表示，探索时空关系。通过在 CHB-MIT 数据集上的实验，模型的灵敏度达到 98.61%，证明了所提出模型的有效性。