Seizures

Can epilepsy be predicted after the first febrile seizure? Insights from machine learning of postictal EEG.

OBJECTIVE: Febrile seizures (FS) are the most common seizures in childhood, yet identifying children at risk of developing epilepsy after the first FS remains challenging. We aimed to evaluate the prognostic potential of machine learning (ML) algorithms applied to post-febrile seizure electroencephalography (EEG) recordings. METHODS: We retrospectively reviewed 104 children (69 boys; mean age at f...

EEG-VLM: A Hierarchical Vision-Language Model With Multi-Level Feature Alignment and Visually Enhanced Language-Guided Reasoning for EEG Image-Based Sleep Stage Prediction.

Sleep stage classification based on electroencephalography (EEG) is fundamental for assessing sleep quality and diagnosing sleep-related disorders. However, most traditional machine learning methods rely heavily on prior knowledge and handcrafted features, while existing deep learning models still struggle to jointly capture fine-grained time-frequency patterns and achieve clinical interpretabilit...

Frequency Band Personalization for Seizure Network Analysis in Multifocal Patients.

Stereo-electroencephalography (SEEG) is commonly used for pre-surgical evaluation in patients with multifocal epilepsy undergoing responsive neurostim...

Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods.

Deep learning for cross-subject EEG decoding is hindered by the high degree of inter-subject variability, which creates a severe domain shift between ...

Deep learning discriminates seizures from normal brain oscillations in the electroencephalogram of a rat model of post-traumatic epilepsy.

This study used machine learning to objectively identify seizures in the electroencephalogram of a model of post-traumatic epilepsy based on fluid per...

EEG-based schizophrenia detection using handcrafted biomarkers and a TOA-optimized hybrid multi-branch CNN-Transformer framework.

Schizophrenia is a chronic psychiatric disorder for which electroencephalography (EEG) offers a low-cost, non-invasive window into abnormal neural dyn...

A Bibliometric Examination of EEGLAB Publications in Scopus and WoS Indexed Sources: A 20-Year Study of Asia-Pacific and Arabian Countries.

IntroductionEEGLAB is a widely used software for analyzing electroencephalography (EEG) datasets, with over 20 years of global use. This bibliometric ...

Integrating EEG microstate dynamics in a stacked ensemble for neurodiagnostic ASD assessment.

Autism Spectrum Disorder (ASD) remains diagnostically challenging due to its neurobiological heterogeneity and the current reliance on subjective beha...

A comparative evaluation of EEG-based deep learning models for schizophrenia detection with cross-dataset validation and explainable AI.

OBJECTIVES: Schizophrenia is a neuropsychiatric disorder that affects emotional, behavioral, and brain functions that can be tracked using electroence...

An EEG-EMG-kinematics dataset from wrist pointing tasks for biomarker research in neurorehabilitation.

This work presents a multimodal dataset containing synchronized electroencephalography (EEG), electromyography (EMG), and kinematic recordings acquire...

RMETNet: A cross-subject motor imagery EEG signal classification model based on TSLANet and riemannian geometry features.

Motor imagery electroencephalogram (MI-EEG) analysis is essential for natural interaction and autonomous control in brain-computer interfaces (BCIs). ...

MRI-based machine learning model to distinguish hippocampal sclerosis (HS) ILAE type 1 and no HS gliosis only in medial temporal lobe epilepsy.

PURPOSE: Despite recent advances in preoperative work-up of drug resistant medial temporal lobe epilepsy (MTLE), predicting post-surgical seizure and ...

Enhancing Target Recognition Performance in SSVEP-Based Brain-Computer Interfaces via Deep Neural Networks with Pyramid Squeeze Attention.

Steady state visual evoked potential (SSVEP)-based brain-computer interfaces have been widely studied for their fast response speeds and high informat...

Learning Where to Look: Differentiable Slice Selection and Efficient Channel Attention for FCD-II MRI Classification.

Focal Cortical Dysplasia (FCD) is a major cause of drug-resistant epilepsy both in children and adults. In most such cases, surgery is the most effect...

CDR-Net: A computerized framework to detect Alzheimer's diseases and mild cognitive impairment.

Alzheimer's disease (AD) and mild cognitive impairment (MCI) are two dementia-related brain illnesses that are prevalent among elders in the twenty-fi...

A motor thalamic site in humans that suppresses involuntary breathing without awareness.

Breathing is generated by brainstem respiratory networks but can be controlled and modulated by forebrain activity. The recent clinical adoption of th...

Persistent white matter disruption underlies apparent functional normalization in intractable temporal lobe epilepsy: Evidence from multimodal MRI.

BACKGROUND: Intractable temporal lobe epilepsy (ITLE) poses ongoing therapeutic challenges due to resistance to antiseizure medications and limited im...

Integrating metacognitive mechanisms optimizes EEG generative models via hierarchical regularization.

Obtaining sufficient electroencephalography (EEG) signals for training deep neural networks (DNNs) in brain-computer interfaces (BCIs) is challenging ...

DeepCRI: Real-time EEG-based Prognostication after Cardiac Arrest.

Accurate prediction of neurological outcome after cardiac arrest is essential for guiding intensive care decisions. Electroencephalography (EEG) suppo...

The Insomnia EEG Score: a new tool for the classification of people with poor sleep.

Quantitative features could help objectively identify and grade insomnia severity, though there is currently no pathophysiological biomarker of insomn...