用3D张量建模脑电波关系，提升癫痫预测准确率
GRC-Net: Gram Residual Co-attention Net for epilepsy prediction
- 将脑电信号转为3D张量，保留时间依赖性并建模跨维度关系
- 在BONN数据集五分类任务中达93.66%准确率，优于现有方法
- 结合共注意力与多粒度结构，适合脑电分析与临床辅助诊断
基于脑电图（EEG）信号的癫痫预测是快速发展的领域。以往研究通常对整个EEG信号进行一维处理。本文采用格拉姆矩阵方法将信号转换为三维表示，可在保持一维信号时间依赖性的同时建模跨维度信号关系。此外，我们观察到EEG数据中局部与全局信号存在不平衡，因此引入多层级特征提取：通过共注意力机制捕捉全局特征，利用Inception结构处理局部信号，实现多粒度特征提取。在BONN数据集上的实验表明，对于最具挑战性的五类分类任务，GRC-Net达到93.66%的准确率，优于现有方法。
原文摘要 · Abstract (English)
Prediction of epilepsy based on electroencephalogram (EEG) signals is a rapidly evolving field. Previous studies have traditionally applied 1D processing to the entire EEG signal. However, we have adopted the Gram Matrix method to transform the signals into a 3D representation, enabling modeling of signal relationships across dimensions while preserving the temporal dependencies of the one-dimensional signals. Additionally, we observed an imbalance between local and global signals within the EEG data. Therefore, we introduced multi-level feature extraction, utilizing coattention for capturing global signal characteristics and an inception structure for processing local signals, achieving multi-granular feature extraction. Our experiments on the BONN dataset demonstrate that for the most challenging five-class classification task, GRC-Net achieved an accuracy of 93.66%, outperforming existing methods.
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