EEG-Titans: Long-Horizon Seizure Forecasting via Dual-Branch Attention and Neural Memory

• URL: http://arxiv.org/abs/2601.13748v1
• Date: Tue, 20 Jan 2026 09:03:49 GMT
• Title: EEG-Titans: Long-Horizon Seizure Forecasting via Dual-Branch Attention and Neural Memory
• Authors: Tien-Dat Pham, Xuan-The Tran,
• Abstract summary: We propose a dual-branch EEG architecture that incorporates modern neural memory mechanism for long-context modeling.<n>EEG-Titans achieves 99.46% average segment-level sensitivity across 18 subjects.<n>Results indicate that memory-augmented long-context modeling can provide robust seizure forecasting under clinically constrained evaluation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurate epileptic seizure prediction from electroencephalography (EEG) remains challenging because pre-ictal dynamics may span long time horizons while clinically relevant signatures can be subtle and transient. Many deep learning models face a persistent trade-off between capturing local spatiotemporal patterns and maintaining informative long-range context when operating on ultralong sequences. We propose EEG-Titans, a dualbranch architecture that incorporates a modern neural memory mechanism for long-context modeling. The model combines sliding-window attention to capture short-term anomalies with a recurrent memory pathway that summarizes slower, progressive trends over time. On the CHB-MIT scalp EEG dataset, evaluated under a chronological holdout protocol, EEG-Titans achieves 99.46% average segment-level sensitivity across 18 subjects. We further analyze safety-first operating points on artifact-prone recordings and show that a hierarchical context strategy extending the receptive field for high-noise subjects can markedly reduce false alarms (down to 0.00 FPR/h in an extreme outlier) without sacrificing sensitivity. These results indicate that memory-augmented long-context modeling can provide robust seizure forecasting under clinically constrained evaluation

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