GRC-Net: Gram Residual Co-attention Net for epilepsy prediction

• URL: http://arxiv.org/abs/2512.12273v1
• Date: Sat, 13 Dec 2025 10:29:28 GMT
• Title: GRC-Net: Gram Residual Co-attention Net for epilepsy prediction
• Authors: Bihao You, Jiping Cui,
• Abstract summary: We have adopted the Gram Matrix method to transform the signals into a 3D representation.<n>We observed an imbalance between local and global signals within the EEG data.<n>Our experiments on the BONN dataset demonstrate that for the most challenging five-class classification task, GRC-Net achieved an accuracy of 93.66%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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