arXiv Machine Learning June 11, 2026 By Elham Rostami, Aref Einizade, Taous-Meriem Laleg-Kirati

SpaTeoGL: Spatiotemporal Graph Learning for Interpretable Seizure Onset Zone Analysis from Intracranial EEG

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 17

NeuroGRIP: Retrieval-Augmented Graph Refinement for Knowledge-Grounded EEG Seizure Diagnosis

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

llms rag safety

arXiv Machine Learning

Jul 23

Adaptive Multi-Expert Graph Transformer for Interpretable EEG-Based Diagnostics

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

llms

arXiv AI

Jun 16

Epileptic Seizure Detection in Separate Frequency Bands Using Feature Analysis and Graph Convolutional Neural Network (GCN) from Electroencephalogram (EEG) Signals

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

safety

arXiv Machine Learning

Jul 30

Detecting seizure onset and offset times using human intelligence: A critical-transitions-based approach

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 Machine Learning

1d ago

M-LINKX: Multiview Graph Learning for Brain Cognitive Disease Detection

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 Machine Learning

Jun 30

Connectivity Estimation using Stochastic Graph Heat Modelling

arXiv:2606. 29098v1 Announce Type: cross Abstract: A growing number of techniques leverage the spatial structures that underlie many real-world datasets.

By Stephan Goerttler, Min Wu, Fei He

safety

Related stories

NeuroGRIP: Retrieval-Augmented Graph Refinement for Knowledge-Grounded EEG Seizure Diagnosis

Adaptive Multi-Expert Graph Transformer for Interpretable EEG-Based Diagnostics

Epileptic Seizure Detection in Separate Frequency Bands Using Feature Analysis and Graph Convolutional Neural Network (GCN) from Electroencephalogram (EEG) Signals

Detecting seizure onset and offset times using human intelligence: A critical-transitions-based approach

M-LINKX: Multiview Graph Learning for Brain Cognitive Disease Detection

Connectivity Estimation using Stochastic Graph Heat Modelling