RAMamba-Net is a new multimodal fusion network designed for auditory attention decoding (AAD) that combines EEG and electrooculography (EOG) signals. It uses a Mamba-enhanced band-aware convolutional Transformer to capture EEG band-specific patterns and long-range temporal dynamics, while a dual-branch encoder models EOG temporal and inter-channel dependencies. Cross‑modal attention and a reliability‑aware module estimate sample‑wise modality weights, improving fusion robustness and achieving a 5.76% accuracy gain over unimodal baselines on two AAD benchmarks.
By Xingyi He, Ziwei Wang, Dongrui Wu
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:2607. 23554v1 Announce Type: cross Abstract: In this paper, we propose the MAEConformer, a novel self-supervised learning framework that combines the Conformer architecture with the Masked Autoencoder (MAE) paradigm for large-scale representation learning from unlabelled electroencephalography (EEG) and heart rate variability (HRV) signals.
By Shuwen Yu, William P Marnane, Geraldine B. Boylan, Gordon Lightbody
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:2506.20354v3 Announce Type: replace-cross
Abstract: Learning from multi-variate time-series with heterogeneous channel configurations remains a fundamental challenge for deep neural networks, p...
By Francesco Carzaniga, Michael Hersche, Abu Sebastian, Kaspar Schindler, Abbas Rahimi
arXiv:2609.15285v1 Announce Type: new
Abstract: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease in which early assessment remains challenging, particularly in low-resou...
By Kadija Abdel Ghader, Emani Babe, Lorenzo Pettinari, Meya Haroune, Sidaty El Hadramy
arXiv:2610.16596v2 Announce Type: cross
Abstract: Seizure prediction and detection, essential for clinical treatment of epilepsy, remain the crucial task of this neurological disease. EEG and iEEG ar...
By Ramya Sundaram, Muhammad Aslam Jarwar, M Uma, Sunder Ali Khowaja, Qurat-Ul-Ain Mastoi
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:2606. 00170v1 Announce Type: cross Abstract: In recent years, emotion recognition based on physiological signals such as electroencephalogram (EEG) has gained considerable attention, as internal physiological data offer greater objectivity and reliability compared to external behavioral data like facial expressions.
By Zheng Wang, Shuo Wang, Junhong Wang
arXiv:2507. 12645v1 Announce Type: cross Abstract: The increasing need for accurate and unified analysis of diverse biological signals, such as ECG and EEG, is paramount for comprehensive patient assessment, especially in synchronous monitoring.
By Mohammed Guhdar, Ramadhan J. Mstafa, Abdulhakeem O. Mohammed
arXiv:2606. 09271v1 Announce Type: cross Abstract: Parkinson's disease (PD) is a progressive neurodegenerative disorder that frequently causes speech impairments associated with hypokinetic dysarthria.
By George Theodosiou, Loukas Ilias, Dimitris Askounis
KnowEEG is a feature‑based EEG classification pipeline that extracts 783 per‑electrode statistical features and cross‑brain connectivity metrics, using a balanced sampling strategy to integrate high‑dimensional per‑electrode data with connectivity information. It achieves performance that matches or surpasses state‑of‑the‑art deep learning models on five diverse tasks—including emotion detection, mental workload, eyes open/closed, abnormal EEG, and event detection—without requiring GPU resources. The approach also offers inherent explainability via feature importance rankings, identifying occipital biomarkers in the eyes open/closed task that align with established neuroscience literature.
By Amarpal Sahota, Navid Mohammadi Foumani, Raul Santos-Rodriguez, Zahraa S. Abdallah