Seizures

Predicting Nitrogen Narcosis Using the Electroencephalogram (EEG) Recorded From Experienced Divers.

Nitrogen narcosis causes acute cognitive impairment in divers breathing compressed air at depth, increasing injury and fatality risk. Susceptibility varies widely between individuals, making real-time cortical monitoring a potential safety tool. We recorded 32-channel electroencephalography (EEG) from 45 divers (both sexes; aged 20-55 years) at rest across four randomized crossover hyperbaric stud...

TriGA-Net: A graph attention network for brain-controlled speaker extraction.

OBJECTIVE: Electroencephalography (EEG)-guided target speaker extraction aims to recover the listener's attended speech from mixed speech. However, effectively representing and integrating the target-related information shared between EEG and speech remains challenging. This study focuses on this challenge. APPROACH: We propose TriGA-Net, a Graph Attention Network for Brain-Controlled Speaker Extr...

Manifold representation learning for Alzheimer's disease detection with EEG-based functional brain connectivity.

Alzheimer's disease (AD) is a common neurodegenerative disease characterized by severe cognitive dysfunctions and brain disorders. The emergence of de...

Pathogenic Genetic Variants, Comorbid Autism and Adaptive Developmental Quotient as Independent Predictors of Intellectual Disability in Children With Global Developmental Delay: An Interpretable Machine Learning Model With Calibrated Risk Estimation.

BACKGROUND: Global developmental delay (GDD) frequently precedes intellectual disability (ID), but no validated multivariable prognostic tool exists t...

Personalized adaptive virtual reality experience driven by electroencephalography-based pain recognition.

BACKGROUND: Non-pharmacological pain management represents an urgent clinical need. Emerging technologies such as virtual reality (VR) and electroence...

Multiple-Classifier Binary Convolutional Siamese Networks for Code-Modulated Visual Evoked Potential Classification.

OBJECTIVE: Non-invasive Brain-Computer Interfaces (BCIs) based on Code-Modulated Visual Evoked Potentials (c-VEPs) using electroencephalography (EEG) ...

Engineering a Timing-Accurate VR-EEG-Oculomotor Fusion System for Objective Assessment of Executive Function in ADHD.

Objective, scalable assessment of executive function in Attention-Deficit/Hyperactivity Disorder (ADHD) is limited by subjective rating scales and sin...

BiGSTF-Net: inter-modal mutual guidance and intra-modal spatio-temporal fusion for EEG-fNIRS cognitive classification.

Objective.Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) provide complementary temporal and spatial information for br...

Evaluating the performance of EEG based on ANN to predict the effectiveness of tDCS combined evaluative conditioning on obsession symptoms reduction in contamination OCD patients: Secondary analysis of data from a randomized controlled trial.

Over 40% of Obsessive-Compulsive Disorder (OCD) patients do not respond to common treatments. This study was a secondary analysis of data from a rando...

Automated segmentation of postsurgical resection cavities on magnetic resonance imaging in focal epilepsy: A Multicentre Epilepsy Lesion Detection study.

OBJECTIVE: Quantitative assessment of extent of tissue resection following epilepsy surgery requires accurate delineation of the resection cavity on p...

The genetic architecture of epilepsy across molecular mechanisms and clinical heterogeneity.

Epilepsy comprises a highly heterogeneous group of neurological disorders unified by a persistent predisposition to recurrent seizures, yet driven by ...

Brain network biomarkers for diagnosis and clinical stratification in multiple sclerosis: a longitudinal study integrating individualised structural covariance MRI with high-density EEG.

BACKGROUND: Multiple sclerosis (MS) is increasingly recognised as a disorder of large-scale brain network reorganisation rather than a disease explain...

Sleep dynamics and epileptogenesis following Kainic acid in epilepsy-susceptible (DBA/2 J) and epilepsy-resistant (C57BL/6) mice.

Susceptibility to epileptogenesis varies in humans and mouse strains. We hypothesized that baseline sleep abnormalities increase susceptibility to epi...

Psychedelics align brain activity with context.

Psychedelics can profoundly alter consciousness by reorganizing brain connectivity1,2, producing acute experiences that shape lasting psychological ch...

Deep Learning for Scalp-Level Nonlinear Source Separation in Electroencephalogram: A Comparative Evaluation of Spatiotemporal Architectures.

Electroencephalography (EEG) is a common technique to measure field potentials of various brain regions, and event-related potentials (ERPs) reflect s...

Dataset distillation for efficient seizure prediction: tiny-scale data, comparable performance.

OBJECTIVE: Deep learning has shown significant potential in electroencephalogram (EEG)-based seizure prediction. However, translating these advances i...

A first realization of reinforcement learning-based closed-loop EEG-TMS.

Transcranial magnetic stimulation (TMS) is a powerful tool to investigate neurophysiology of the human brain and treat brain disorders. Traditionally,...

EEG-based brain-computer interface (BCI) dataset for directional word recognition.

We present an EEG dataset recorded from 22 neurologically healthy volunteers (12 native Russian speakers and 10 native Spanish speakers) during overt ...

Seizure detection using hierarchical temporal trend integration and self-supervised learning.

Early, reliable detection of epileptic seizures from electroencephalography (EEG) remains challenging due to label scarcity, inter patient variability...

Predicting big five personality traits from resting-state EEG connectivity: a connectome predictive modeling study.

Personality traits are stable individual differences linked to important life outcomes including mental health, occupational functioning, and interper...