Seizures

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices

Real-time EEG classification on edge devices is bottlenecked by the floating-point arithmetic of conventional neural networks. We investigated Differentiable Logic Gate Networks (Diff-Logic) as a hardware-native alternative that compiles models into pure Boolean circuits executable via bitwise CPU operations. Through rigorous iso-parameter experiments across four EEG datasets spanning two classifi...

A Preoperative Electroencephalography Signature for Predicting Treatment Response to Deep Brain Stimulation in Obsessive-Compulsive Disorder

Deep brain stimulation (DBS) is effective for treatment-refractory obsessive-compulsive disorder (OCD), but outcomes are heterogeneous and non-responders incur surgical and financial burden. We sought a scalable, non-invasive preoperative signature of DBS response. Using preoperative resting-state electroencephalography (EEG) from a randomized, double-blind, sham-controlled trial of nucleus accumb...

Physiological Prior-Driven Label Enhancement for Cross-Subject EEG Emotion Recognition

Electroencephalography (EEG)-based emotion recognition captures affective neural signals with high temporal precision, but cross-subject variability a...

Benchmarking MRI Representations for Deep Learning-Based Focal Cortical Dysplasia Segmentation

Focal cortical dysplasia (FCD) is one of the leading structural causes of drug-resistant focal epilepsy, yet its subtle and heterogeneous imaging char...

Deep Learning Approaches for Sleep Apnea Classification from Polysomnographic EEG Signals

Sleep apnea diagnosis via polysomnography remains resource intensive and relies on time consuming manual data analysis and scoring. Recent work has de...

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

Seizure diagnosis from EEG signals is a critical yet persistently challenging task, due to the complicated neural dynamics and the spurious connection...

Lost in Visual Translation: A VLM-Assisted Perceptual-Semantic Coherence Framework for EEG-to-Image Reconstruction

EEG-to-image evaluation should distinguish visual fidelity from recoverable meaning. Yet EEG-derived reconstructions are blurry, distorted, and low-de...

DiffEEG: A Self-Supervised Denoising Diffusion Model for Learning EEG Generic Representations

Deep learning for EEG-based seizure detection faces critical challenges: severe annotation scarcity and extreme class imbalance, where ictal events co...

Automated Interpretation of EEG Reports Using a Large Language Model with Structured Confidence Outputs

Background: Free-text EEG reports typically lack structure, hindering scalable analysis. We evaluate a large language model (LLM) pipeline to extract ...

Neonatal Seizure Detection Using Combined aEEG and Compressed Spectral Array Features: A Machine-Learning Proof-of-Concept Study

Background To build a clinically translatable neonatal seizure detection algorithm using amplitude-integrated electroencephalography (aEEG) and compre...

Uncertainty-aware extraction of clinical findings from Finnish EHRs using open large language models

Objective. To evaluate whether open-weight large language models (LLMs) can accurately extract clinical findings from Finnish-language pediatric recor...

BCCWJ-Brain: A Multi-Modal fMRI, MEG, and EEG Dataset of Naturalistic Japanese Reading

We present the BCCWJ-Brain dataset, a multi-modal neuroimaging resource comprising functional magnetic resonance imaging (fMRI), magnetoencephalograph...

A scalable neuroinformatics pipeline for harmonizing routine clinical electroencephalograms across public hospitals

We propose a study protocol for routine clinical electroencephalograms (EEGs) from public hospitals, which represents a vast resource for neuroscience...

Effects of EEG Preprocessing on Channel-Wise Attention and Effective Connectivity Alignment in Visual EEG Decoding

Transformer-based deep learning models have shown great potential for decoding visual EEG signals. However, their internal attention mechanisms are of...

Computational demands shape seizure susceptibility in recurrent neural networks

Brain areas differ in their inherent susceptibility to focal seizures, but the principles governing this risk remain unclear. While prior work has foc...

Validation of aEEG-CSA Neonatal Seizure Detection Algorithm on Hypothermia Treated Infants with HIE

Abstract Objective To validate a neonatal seizure detection algorithm that is based on extracted clinical features of the aEEG and CSA on a cohort of ...

Hemispheric Asymmetry Features and Interpretable Machine Learning for Focal Cortical Dysplasia Classification in Drug-Resistant Epilepsy

Focal cortical dysplasia (FCD) is a principal cause of pharmacoresistant focal epilepsy, yet its structural MRI signature, subtle cortical thickening,...

EEG-SpikeAgent: Agentic Closed-Loop Program Synthesis for Automated EEG Spike Detection

Automated detection of interictal epileptiform discharges in scalp electroencephalography (EEG) is clinically important, but recent high-performing de...

Lightweight ML-Based Automatic Sleep Staging Framework with Constrained CNN and Mamba for Small-Sample EEG Datasets

Automatic sleep staging is a key technology for precise diagnosis and treatment of sleep disorders as well as long-term home sleep monitoring. Portabl...

Wavelet Scattering Transform for Interpretable Schizophrenia Biomarker Discovery and Classification from Resting-State EEG

Schizophrenia is a debilitating neuropsychiatric disorder characterized by profound cortical network dysregulation, for which objective, clinically tr...