Seizures

Dual MDM2 and PPARG regulatory axes mediate the antiepileptic effects of Gouteng: An Integration of Bioinformatics, Network Pharmacology, Machine Learning, Molecular Docking, and MD Simulations.

Gouteng is a traditional Chinese medicine widely used for the clinical treatment of epilepsy, yet the specific mechanism underlying its antiepileptic has not been elucidated. Network pharmacology, bioinformatics, machine learning, molecular docking, MD simulation, and ADMET druggability analysis were performed to systematically elucidate the molecular regulatory mechanism of Gouteng against epilep...

Clinical application of artificial intelligence technology in epilepsy.

Epilepsy is a prevalent neurological disorder, and its inherent complexity and significant interindividual variability pose substantial challenges for clinical diagnosis and treatment. Against this backdrop, the rapid development of artificial intelligence (AI) technology is driving a historic paradigm shift in the field of epilepsy diagnosis and management. This review analyses the application of...

Exploration of EEG activity corresponding to the functional expansion theory of disgust.

Disgust is often conceptualised as a basic emotion arising from contamination avoidance; however, the functional expansion account distinguishes multi...

A multi-band spatial asymmetry convolutional neural network for EEG-based emotion recognition.

Emotion recognition is an important component for enabling machines to perceive and respond to human emotions. Existing electroencephalogram (EEG)-bas...

Deep learning for EEG-based depression detection: A systematic literature review of methods and interpretability techniques.

Depression is a widespread mental illness in which there is a noticeable difference in the quality of life of people. Electroencephalography (EEG) see...

Altered Temporal dynamics of resting-state EEG microstates as a neural signature of impaired consciousness: a high-density EEG analysis in chronic disorders of consciousness.

PRIMARY OBJECTIVE: To differentiate Unresponsive Wakefulness Syndrome (UWS) from Minimally Conscious State (MCS) using EEG microstates. RESEARCH DESIG...

Mental workload classification using EEG in drivers: A personalized approach to occupational risk reduction.

BackgroundMental state is a key factor influencing occupational health and safety, particularly in professional drivers who are exposed to high cognit...

Automated sleep scoring in hibernating and non-hibernating American black bears.

Hibernating bears show remarkable metabolic suppression. Their decline in core body temperature (Tb) is moderate (from 38°C to 30-35°C), but their met...

Timely Surgical Approaches for Pediatric Epilepsy Resistant to Medication.

Meckel's Pediatric drug-resistant epilepsy (DRE) is a significant neurological disorder that develops when seizures persist despite treatment with two...

Long-term prediction of epilepsy following traumatic brain injury among veterans using routine clinical data.

OBJECTIVE: Despite elevated risk for epilepsy following traumatic brain injury (TBI), there are limited tools to assess epilepsy risk following TBI us...

Music emotion recognition with cEEGrid.

OBJECTIVE: Emotion decoding is a growing field of electroencephalography (EEG) research with numerous applications in areas such as healthcare, partic...

Nonlinear neurodynamics of N2 sleep EEG predict outcomes of anterior nucleus of the thalamus deep brain stimulation in epilepsy: A pilot study.

OBJECTIVE: Anterior nucleus of the thalamus deep brain stimulation (ANT-DBS) is an effective therapeutic option for drug-resistant epilepsy (DRE); how...

Synergistic EEG signal processing for brain-computer interfaces using hybrid MothCray optimization and deep learning.

Electroencephalography (EEG) based brain-computer interface (BCI) systems require optimal channel selection to achieve high signal quality, reduced se...

Assessing a reduced-channel algorithm for end-to-end seizure detection on multiday EEG using inter-rater agreement with epileptologists.

Automated electrographic seizure detection software often rely on the high channel counts of traditional electroencephalography (EEG) recording system...

Validation of a deep learning-based system for sleep staging, arousal detection, and respiratory event scoring in atrial fibrillation patients.

PURPOSE: Obstructive sleep apnea is a common yet underdiagnosed sleep disorder in patients with atrial fibrillation (AF). Although polysomnography (PS...

EEG-fMRI fusion-based source localization for the identification and mechanistic elucidation of paroxysmal kinesigenic dyskinesia.

UNLABELLED: Paroxysmal kinesigenic dyskinesia is a rare neurological disorder characterized by brief, recurrent motor attacks that significantly impai...

Leveraging pretrained vision transformers for classifying alcohol use disorder using raw resting-state EEG.

Alcohol Use Disorder (AUD) is a prevalent neuropsychiatric condition affecting about 28 million adults in the USA, with few objective biomarkers to as...

A network approach with motion sequencing reveals hidden patterns of repetitive behavior in a pre-clinical model of epilepsy.

Epilepsy is the 4th most prevalent neurological condition with 50 million cases worldwide. Patients with epilepsy bare a disproportionate burden of co...

Enhancing the Capability and Accuracy of Motor Imagery Classification: A Deep Neural Network-Powered Multifaceted Strategy Model.

Motor imagery (MI) is a popular noninvasive brain computer interface (BCI) paradigm, yet its decoding accuracy remains hindered by the inherent nonsta...

The role of catastrophizing in anxiety and depression symptom scores among knee osteoarthritis patients: a multimodal assessment with EEG and machine learning.

To examine dimensional associations between anxiety- and depression-related symptom severity, pain catastrophizing, and resting-state EEG features in ...