The paper introduces HyBrain, a model that uses spatiotemporal hyperedges to capture coordinated EEG seizure activity more efficiently than pairwise edge approaches. By generating one token per channel per second and pooling them into shared hyperedge embeddings, HyBrain performs well across detection and prediction tasks on TUSZ and CHB‑MIT datasets, achieving top AUROC scores and competitive training resources. A qualitative example shows a single hyperedge tracking the preictal‑ictal‑postictal progression of a real seizure.
By Hyunju Kim, Sheo Yon Jhin, Noseong Park, Nabil Imam
The study evaluates how different graph topologies influence the localization of the epileptogenic zone (EZ) from resting‑state stereo‑EEG recordings in 40 patients. Using a consistent learning model and leave‑one‑patient‑out validation, the authors compare dense graphs, anatomy‑ and geometry‑informed priors, budgeted sparsification, learned sparsification, and a new Region‑Bridge‑c topology. At about 30% edge retention, Region‑Bridge‑c achieves the highest mean PR‑AUC and ROC‑AUC while using roughly 69% fewer edges than a dense graph, indicating that graph construction significantly impacts EZ localization performance.
By Daniel Wendelken (University of Cincinnati, Cincinnati, USA), Brian Ervin (Cincinnati Children's Hospital Medical Center, Cincinnati, USA), Ravindra Arya (Cincinnati Children's Hospital Medical Center, Cincinnati, USA), Ali A. Minai (University of Cincinnati, Cincinnati, USA)
arXiv:2607. 14314v1 Announce Type: new Abstract: Seizure diagnosis from EEG signals is a critical yet persistently challenging task, due to the complicated neural dynamics and the spurious connections in inter-channel modeling.
By Lincan Li, Zheng Chen, Yushun Dong
arXiv:2607. 19429v1 Announce Type: new Abstract: Electroencephalographic (EEG) abnormalities arise from dynamic changes in neural synchrony across spatial and temporal scales, yet many computational approaches reduce these dynamics to static features.
By Maryam Rahimimovassagh, Md Elias Hossain, Ivan Garibay, Niloofar Yousefi
arXiv:2604. 00163v2 Announce Type: replace-cross Abstract: Epileptic seizures are neurological disorders characterized by abnormal and excessive electrical activity in the brain, resulting in recurrent seizure events.
By Ferdaus Anam Jibon, Fazlul Hasan Siddiqui, F. Deeba, Gahangir Hossain
Forecasting time-varying functional connectivity from electroencephalography (EEG) requires modeling both history-dependent trends and structured variability across channels. Conditional flow matching...
arXiv:2609.37934v1 Announce Type: new
Abstract: Forecasting time-varying functional connectivity from electroencephalography (EEG) requires modeling both history-dependent trends and structured varia...
By Haohui Jia, Zheng Chen, Jathurshan Pradeepkumar, Xu Cao, Yasuko Matsubara, Yasushi Sakurai, Takashi Matsubara
arXiv:2607. 27105v1 Announce Type: cross Abstract: Most existing seizure detection algorithms require extensive pre-processing of the data and rely on heuristic or currently unexplainable machine learning approaches.
By Andrew Flynn, Cian McCafferty, Klaus Lehnertz, Fran\c{c}ois David, Vincenzo Crunelli, Gordon Lightbody, Sebastian Wieczorek
arXiv:2608.29755v1 Announce Type: new
Abstract: Electroencephalography (EEG) is a promising, non-invasive, and cost-effective modality for Alzheimer's disease (AD) detection, but deep learning method...
By M. Tanveer, Ayush Singh Rana, Sanskriti Jain, Arnav Kumar, Aryaman Tiwari, A. Rahaman, A. Quadir, M. Sajid
arXiv:2608. 14847v1 Announce Type: new Abstract: Electroencephalogram (EEG) is a non-invasive and relatively low-cost procedure that measures brain electricity for the detection of cognitive diseases.
By An Phan, Yufei Jin, Xingquan Zhu
arXiv:2608.21445v1 Announce Type: new
Abstract: Automated seizure detection from electroencephalography (EEG) is essential for continuous neurological monitoring, particularly for subclinical epilept...
By Chenxi Liu, Mingzhao Li, Yicong Liu, Hao Miao, Hongyuan Zhang, Ziyi Chen, Gaofeng Meng
The paper introduces a new framework for learning time‑varying graphs from spatiotemporal data, leveraging a prior on signal temporal behavior to estimate graphs from few measurements. It adds three convex regularization terms that enforce sparsity in the temporal changes of the network, and presents a scalable algorithm to solve the resulting optimization problem. Experiments on synthetic data and real datasets—including point clouds, temperature readings, and EEG signals—show that the method outperforms existing state‑of‑the‑art approaches.
By Haruki Yokota, Koki Yamada, Yuichi Tanaka, Antonio Ortega