Seizures

UKF-based model parameter estimation to localize the seizure onset zone in ECoG.

Drug-resistant epilepsy (DRE) patients typically require surgical intervention or neurostimulation. Therefore, accurate localization of the seizure onset zone (SOZ) is essential for effective clinical intervention. Although some physiologically meaningful parameters of neural computational models show substantial differences across brain regions during seizures, few studies pay attention to applyi...

Multi-branch GAT-GRU-transformer for explainable EEG-based finger motor imagery classification.

Electroencephalography (EEG) provides a non-invasive and real-time approach to decoding motor imagery (MI) tasks, such as finger movements, offering significant potential for brain-computer interface (BCI) applications. However, due to the complex, noisy, and non-stationary nature of EEG signals, traditional classification methods-such as Common Spatial Pattern (CSP) and Power Spectral Density (PS...

Network models incorporating chloride dynamics predict optimal strategies for terminating status epilepticus.

Status epilepticus (SE), seizures lasting beyond five minutes, is a medical emergency commonly treated with benzodiazepines which enhance GABA recepto...

An EEG-EMG dataset from a standardized reaching task for biomarker research in upper limb assessment.

This work describes a dataset containing high-density EEG (hd-EEG) and surface electromiography (sEMG) to capture neuromechanical responses during a r...

Diagnosis of epileptic seizure neurological condition using EEG signal: a multi-model algorithm.

INTRODUCTION: Affecting millions of individuals worldwide, epilepsy is a neurological condition marked by repeated convulsions. Monitoring brain activ...

Efficient system for classifying cyclic alternating pattern phases in sleep.

Electroencephalogram (EEG) signals are a popular tool to analyze sleep patterns. Cyclic alternating patterns (CAP) can be observed in EEG signals duri...

PhyTransformer: A unified framework for learning spatial-temporal representation from physiological signals.

As a modal of physiological information, electroencephalogram (EEG), surface electromyography (sEMG), and eye tracking (ET) signals are widely used to...

Neurophysiological Approaches to Lie Detection: A Systematic Review.

Lie detection is crucial in domains such as security, law enforcement, and clinical assessments. Traditional methods suffer from reliability issues a...

Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods.

Driven by the remarkable capabilities of machine learning, brain-computer interfaces (BCIs) are carving out an ever-expanding range of applications ac...

Decision support system based on ensemble models in distinguishing epilepsy types.

This study aimed to classify patients' focal (frontal, temporal, parietal, occipital), multifocal, and generalized epileptiform activities based on EE...

MT-RCAF: A Multi-Task Residual Cross Attention Framework for EEG-based emotion recognition and mood disorder detection.

BACKGROUND AND OBJECTIVE: Prolonged abnormal emotions can gradually evolve into mood disorders such as anxiety and depression, making it critical to s...

Quantitative electroencephalogram and machine learning to predict expired sevoflurane concentration in infants.

Processed electroencephalography (EEG) indices used to guide anesthetic dosing in adults are not validated in young infants. Raw EEG can be processed ...

Lightweight hybrid transformers-based dyslexia detection using cross-modality data.

Early and precise diagnosis of dyslexia is crucial for implementing timely intervention to reduce its effects. Timely identification can improve the i...

A systematic review of artificial intelligence techniques based on electroencephalography analysis in the diagnosis of epilepsy disorders: A clinical perspective.

In recent years, Artificial Intelligence (AI), with a specific emphasis on attention mechanisms instead of conventional Deep Learning (DL) or Machine ...

Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types.

This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-Computer Interface (BCI...

The Lack of Neurofeedback Training Regulation Guidance and Process Evaluation May be a Source of Controversy in Post-Traumatic Stress Disorder-Neurofeedback Research: A Systematic Review and Statistical Analysis.

Neurofeedback (NF) based on brain-computer interface (BCI) is an important direction in adjunctive interventions for post-traumatic stress disorder (...

SyncLearnNet: Generalized Epileptic Seizure Detection Network Based on Brain Signals.

Epilepsy is a prevalent neurological disorder with significant detrimental effects on health. Accurate seizure detection is crucial for the precise di...

Quantifying Sleep Quality Through Delta-Beta Coupling Across Sleep and Wakefulness.

Modern lifestyles contribute to insufficient sleep, impairing cognitive function and weakening the immune system. Sleep quality (SQ) is vital for phys...

Clinical Value of ChatGPT for Epilepsy Presurgical Decision-Making: Systematic Evaluation of Seizure Semiology Interpretation.

BACKGROUND: For patients with drug-resistant focal epilepsy, surgical resection of the epileptogenic zone (EZ) is an effective treatment to control se...

Seizures influence sleep macrostructure and the sleep-wake circadian rhythm in Dravet syndrome.

OBJECTIVE: Dravet syndrome (DS) is a developmental and epileptic encephalopathy with a wide spectrum of comorbidities comprising sleep disorders, repo...