The paper introduces a deep learning framework that uses a CNN‑GRU architecture to classify EEG recordings into resting or cognitive states. Time‑frequency analysis extracts salient signal features, which are then evaluated with both deep learning and traditional machine learning classifiers. The proposed method achieves accuracies of 83.177% for resting vs. mathematical tasks, 76.107% for resting vs. memory tasks, and 83.432% for resting vs. music tasks, outperforming comparative approaches.
By K. A. Januka S. Fernando, Harshit Srivastava
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 deep learning framework that uses a CNN‑GRU architecture to classify EEG recordings into resting and various cognitive states. Time‑frequency analysis is applied to extract salient signal features, which are then evaluated with both deep learning and traditional machine learning classifiers, including a proposed 2D‑Net. The method achieves accuracies of 83.177% for resting vs. mathematical tasks, 76.107% for resting vs. memory tasks, and 83.432% for resting vs. music tasks, outperforming comparative approaches.
arXiv:2606. 02166v1 Announce Type: new Abstract: Epilepsy is one of the most common neurological disorders globally, characterized by recurring seizures and significantly impacting the quality of life.
By Vigneshwar Hariharan (National University of Singapore), Chithra Reghuvaran (University College Dublin), Arlene John (University of Twente), Nhat Pham (Cardiff University), Omer Rana (Cardiff University), Deepu John (University College Dublin), Ganesh Neelakanta Iyer (National University of Singapore)
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:2609.22141v1 Announce Type: cross
Abstract: Automated seizure detection from scalp electroencephalography (EEG) is difficult because seizure morphology varies among patients and seizure samples...
By Maimuna Chowdhury, Sk. Imran 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:2510. 11917v2 Announce Type: replace Abstract: Dementia disorders such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) exhibit overlapping electrophysiological signatures in EEG that challenge accurate diagnosis.
By Jun-En Ding, Anna Zilverstand, Shihao Yang, Albert Chih-Chieh Yang, Feng Liu
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: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
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. 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