Seizures

Image-grounded encoding models reveal distinct temporal profiles of naturalistic object and scene processing in the human brain

The human brain processes large amounts of complex visual information in order to effectively interact with its environment. It is well established that our visual system has specialized regions to process incoming information efficiently, such as scene-, face-, and object-selective areas, which can be uncovered using functional magnetic resonance imaging (fMRI). However, mapping of specialized vi...

Subject-Aware Multi-Granularity Alignment for Zero-Shot EEG-to-Image Retrieval

Zero-shot EEG-to-image retrieval aims to decode perceived visual content from electroencephalography (EEG) by aligning neural responses with pretrained visual representations, providing a promising route toward scalable visual neural decoding and practical brain-computer interfaces. However, robust EEG-to-image retrieval remains challenging, because prior methods usually rely on either a single fi...

Predicting Post-Traumatic Epilepsy from Clinical Records using Large Language Model Embeddings

Objective: Post-traumatic epilepsy (PTE) is a debilitating neurological disorder that develops after traumatic brain injury (TBI). Early prediction of...

Neural mechanism of postural sway-related beta-band oscillations: a cortico-basal ganglia-thalamic network model of intermittent control

Recent EEG studies of human quiet stance have identified beta-band event-related desynchronization (beta-ERD) and synchronization (beta-ERS; post-move...

EEG-Based Multimodal Learning via Hyperbolic Mixture-of-Curvature Experts

Electroencephalography (EEG)-based multimodal learning integrates brain signals with complementary modalities to improve mental state assessment, prov...

Classification of Epileptic iEEG using Topological Machine Learning

Epileptic seizure detection from EEG signals remains challenging due to the high dimensionality and nonlinear, potentially stochastic, dynamics of neu...

EEG2Vision: A Multimodal EEG-Based Framework for 2D Visual Reconstruction in Cognitive Neuroscience

Reconstructing visual stimuli from non-invasive electroencephalography (EEG) remains challenging due to its low spatial resolution and high noise, par...

Brain3D: EEG-to-3D Decoding of Visual Representations via Multimodal Reasoning

Decoding visual information from electroencephalography (EEG) has recently achieved promising results, primarily focusing on reconstructing two-dimens...

Optimizing EEG Graph Structure for Seizure Detection: An Information Bottleneck and Self-Supervised Learning Approach

Seizure detection from EEG signals is highly challenging due to complex spatiotemporal dynamics and extreme inter-patient variability. To model them, ...

BrainYears: A functional EEG-based brain age clock enables intervention-ready measurements of brain aging

Biological brain aging is a major determinant of cognitive decline and neurodegenerative disease, yet scalable and intervention-ready brain aging biom...

Narcolepsy Revolution - Protocol and Methodology A diagnostic accuracy study protocol using the Dreem 3 headband for ambulatory diagnosis of narcolepsy in children and young adults

Background Narcolepsy is a rare, lifelong neurological disorder that often begins in childhood or adolescence. Diagnosis is frequently delayed because...

EEG Foundation Model Improves Online Directional Motor Imagery Brain-computer Interface Control

Brain-Computer interfaces (BCIs) offer a link between neural signals and external computation, enabling control of devices for the purposes of restori...

Altered EEG markers of reward learning during abstinence in alcohol dependence: a probabilistic reversal learning study

Maladaptive reward learning and decision-making circuity are key factors in the onset and progression of alcohol use disorder and have therefore emerg...

Preventing Data Leakage in EEG-Based Survival Prediction: A Two-Stage Embedding and Transformer Framework

Deep learning models have shown promise in EEG-based outcome prediction for comatose patients after cardiac arrest, but their reliability is often com...

EEG-SSFormer: Towards a Robust Mamba-Based Architecture for Dementia Detection from Resting State Electroencephalography

Resting-state electroencephalography (rs-EEG) offers a cost effective and portable alternative to conventional neuroimaging for dementia screening, ye...

Spectral and non-spectral EEG measures in the prediction of working memory task performance and psychopathology

Working memory (WM) supports the temporary maintenance of goal-relevant information and is disrupted across many neuropsychiatric disorders. We examin...

Modeling Spatiotemporal Neural Frames for High Resolution Brain Dynamic

Capturing dynamic spatiotemporal neural activity is essential for understanding large-scale brain mechanisms. Functional magnetic resonance imaging (f...

AI Generalisation Gap In Comorbid Sleep Disorder Staging

Accurate sleep staging is essential for diagnosing OSA and hypopnea in stroke patients. Although PSG is reliable, it is costly, labor-intensive, and m...

Learning Cross-Joint Attention for Generalizable Video-Based Seizure Detection

Automated seizure detection from long-term clinical videos can substantially reduce manual review time and enable real-time monitoring. However, exist...

A Clinical Guideline-Grounded Hybrid Agentic Framework for Holistic Epilepsy Management.

Epilepsy is a chronic neurological disorder requiring multi-faceted management, including seizure detection, syndrome diagnosis, prognostication, anti...