arXiv AI

Towards Modality-Agnostic Seizure Classification: A Unified Deep Learning Framework for Heterogeneous EEG and Intracranial EEG Signals

arXiv Machine Learning
Jun 2

EEG-FuseFormer: A Transformer-Driven Feature Fusion Framework for Seizure Onset Prediction

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 Machine Learning
Sep 4

RobustSeiz: An Open-Source Framework for Benchmarking the Robustness of EEG Seizure Detection Models

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 Machine Learning
Aug 4

SingLEM: Single-Channel Large EEG Model

arXiv:2509. 17920v2 Announce Type: replace Abstract: Current deep learning models for electroencephalography (EEG) are often task-specific and depend on large labeled datasets, limiting their adaptability.

By Jamiyan Sukhbaatar, Satoshi Imamura, Ibuki Inoue, Shoya Murakami, Kazi Mahmudul Hassan, Seungwoo Han, Ingon Chanpornpakdi, Toshihisa Tanaka
arXiv AI
Sep 15

Schizophrenia Detection from EEG Signals: A Transformer Framework with Spectrogram Representation

The paper presents a transformer-based framework for detecting schizophrenia from EEG signals. It converts preprocessed EEG recordings into spectrograms via Short-Time Fourier Transform and classifies them using both traditional machine learning algorithms and deep learning models, including CNN-Transformer hybrids. Subject-level data partitioning ensures reliable evaluation, yielding an AUC-ROC of 88.41% for the CNN-Transformer model and 92.88% for the CNN + Squeeze and Excitation + Transformer model on an independent test set.

By Abtin Shafiei, Mohsen Hooshmand, Majid Ramezani