RobustSeiz is an open‑source, model‑agnostic framework designed to benchmark the robustness of EEG seizure detection models under realistic clinical stressors. It standardizes four public scalp‑EEG corpora into BIDS‑EEG trees, applies controlled distribution shifts—including environmental, noise, and adversarial transforms—across predefined hyperparameter grids, and reports comprehensive performance metrics such as sensitivity, precision, F1, false positives per 24 h, onset timing, and predictive agreement. The framework offers a Dockerized GPU pipeline, experiment registry, and both full‑evaluation and research‑subset modes, and demonstrates its utility by evaluating a contemporary detector on TUSZ across the full shift grid.
By Mohammad Mohammadi, Alireza Zarei
arXiv:2609.35876v1 Announce Type: new
Abstract: Epileptic seizure forecasting aims to provide actionable warnings before seizure onset, yet patient-independent generalization and false-alarm control...
By Mason Dana, Khandaker Mamun Ahmed
arXiv:2508.04899v3 Announce Type: replace
Abstract: Reliable evaluation of machine learning models for neonatal seizure detection is critical for clinical adoption. Current practices often rely on in...
By Jovana Kljajic, John M. O'Toole, Robert Hogan, Tamara Skoric
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
arXiv:2607. 21384v1 Announce Type: new Abstract: Electroencephalography (EEG) models used for epilepsy are often limited to specific datasets and tasks.
By Targol Bakhtiarvand, Jugal Kalita, Adham Atyabi
arXiv:2609.36087v1 Announce Type: cross
Abstract: Clinicians and neuroscientists have long analyzed intracranial electroencephalography (iEEG) through directly measurable physiological characteristic...
By Yipeng Zhang, Chenda Duan, Yuanyi Ding, Tianyi Wang, Atsuro Daida, Masaki Izumi, Yuta Tanoue, Naoto Kuroda, Shaun A. Hussain, Nishant Sinha, Eishi Asano. Hiroki Nariai, Vwani Roychowdhury
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
arXiv:2609.36609v1 Announce Type: cross
Abstract: Electroencephalography (EEG) analysis requires careful choices in preprocessing, statistical modeling, and machine learning because EEG signals are h...
By Parsa Razmara, Woojae Jeong, Aditya Kommineni, Raymundo Cassani, Richard Leahy, Takfarinas Medani
arXiv:2602. 11801v2 Announce Type: replace Abstract: Accurate localization of the seizure onset zone (SOZ) from intracranial EEG (iEEG) is essential for epilepsy surgery but is challenged by complex spatiotemporal seizure dynamics.
By Elham Rostami, Aref Einizade, Taous-Meriem Laleg-Kirati
The paper introduces a benchmark for out‑of‑distribution (OOD) detection in electroencephalography (EEG) machine learning, evaluates a wide range of OOD methods, and assesses their impact on two clinical downstream prediction tasks. It distinguishes between OOD detection and model uncertainty estimation, which are often conflated, and shows how combining complementary methods can create a robust safety net for deploying EEG‑based models in high‑risk settings.
By Philipp Bomatter, Henry Gouk
arXiv:2607. 16296v1 Announce Type: cross Abstract: Continuous EEG monitoring for epilepsy is constrained by the limited power and memory budgets of wearable and implantable devices.
By Kartikey Ahlawat
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