Seizures

Artificial intelligence in the assessment of epilepsy-related genetic mutations: Learned from GABAA receptors and GABA transporter 1.

This review examines how recent genetic and technological advances have transformed our understanding and treatment of genetic epilepsies (GEs), with a focus on disorders involving GABAA receptors (GABRs) and the GABA transporter 1 (GAT-1) encoded by SLC6A1. About 1000 genes are associated with epilepsy, including ~100 directly linked to defined epilepsy syndromes. Many disease-causing variants af...

Cerebro Wave Bee transformer to leverage EEG based hand movement classification using optimized bio-inspired learning rates.

Electroencephalogram (EEG) based classification of hand movements have an eloquent significance in the diverse fields like biomedical engineering, neuroscience, and assistive technologies. EEG can be used to convert brain waves into useful commands supporting multiple tasks. The article introduces a novel EEG-based hand-movement classification using Honey Bee Optimization (HBO) as an offline hyper...

MSFSNet: Multi-Source Few-Shot Adaptation Network for Cross-Subject Depression Recognition from EEG Signals.

Depression is a prevalent mental disorder with severe socio-economic implications, and its early identification and intervention are crucial for mitig...

A multifeature machine learning and resting-state EEG study reveals differences in beta oscillation in late-life depression with or without mild cognitive impairment.

BACKGROUND: Late-life depression (LLD) often co-occurs with mild cognitive impairment (MCI), and patients with LLD and MCI (LLD-MCI) have an increased...

Research on depression diagnosis method based on multi-scale analysis of frontal lead EEG.

BACKGROUND: Depression is one of the most prevalent mental disorders globally, severely affecting individuals' emotional, cognitive, and physical func...

Adaptive multimodal learning for driver cognitive state monitoring using transformer-based fusion with personalized meta-learning and federated optimization.

Road accidents caused by driver fatigue and cognitive overload remain a significant public safety concern. According to recent traffic safety data, dr...

EEG-based harmful brain activity classification using deep learning and feature fusion.

The prevalence of research on harmful brain activity has increased, especially since the standardization of electroencephalography (EEG) terminologies...

Decoding the neural mechanisms of salty peptide perception via electroencephalography and machine learning.

Excessive sodium intake poses major public health risks, driving the search for salt substitutes that preserve desirable flavor. Salty peptides have e...

Identification of EEG features during status epilepticus for prediction of emergent epilepsy phenotype in the mouse intra-amygdala kainic acid model using supervised learning.

Preclinical animal models are essential for investigating epilepsy mechanisms and evaluating novel therapies. In rodents, epilepsy can be induced by s...

Federated learning-enabled privacy-preserving framework for seizure forecasting and affective state analysis using multi-modal EEG-ECG data.

Seizure forecasting and affective state analysis using EEG-ECG data play a pivotal role in advancing neurological and mental health monitoring. Howeve...

Self-Supervised Contrastive Pre-Training for EEG-Based Recognition via Cross Device Representation Consistency.

Electroencephalography (EEG) has emerged as a powerful tool for modeling human brain states. However, the widespread adoption of EEG-based recognition...

SemSTNet: Medical EEG Semantic Metric Learning With Class Prototypes Generated by Pretrained Language Model.

Electroencephalography (EEG) feature learning is crucial for brain-machine interfaces and medical diagnostics. Existing deep learning models for class...

Interpretable Machine Learning to Anticipate the Diagnostic Yield of EEG in the Emergency department. The EMINENCE study.

INTRODUCTION: Emergent electroencephalography (emEEG) is increasingly employed in the emergency department (ED) for evaluating altered consciousness a...

Deep learning for EEG-based sleep stage classification: a review.

Deep learning architectures are now widely applied in sleep electroencephalogram (EEG) analysis. These developments have significantly advanced EEG-ba...

Microstate permutation complexity of EEG signals distinguishes minimally conscious state plus from minimally conscious state minus.

BACKGROUND: Accurately distinguishing minimally conscious state plus (MCS+) from minimally conscious state minus (MCS-) is critical for prognosis and ...

Subject-Independent Deep Learning Framework for Motor Imagery Electroencephalogram Decoding in Neurorehabilitation.

Motor imagery (MI) has emerged as a pivotal paradigm in non-invasive brain-computer interfaces (BCIs) for neurorehabilitation, enabling motor function...

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models.

Event-related potential (ERP), a specialized paradigm of electroencephalographic (EEG), reflects neurological responses to external stimuli or events,...

Complexity of resting cortical activity predicts neurophysiological responses to theta-burst stimulation but fails to generalize: A rigorous machine-learning approach.

BACKGROUND: Substantial variability in individual responses to intermittent theta-burst stimulation (iTBS) limits its clinical efficacy, yet neurophys...

Exploratory decoding of TMS-EEG: Predicting TEP response to intermittent and continuous theta burst stimulation.

Theta burst stimulation (TBS) is a promising form of repetitive transcranial magnetic stimulation (rTMS) capable of modulating cortical excitability a...

Explainable artificial intelligence-driven visual task-specific electroencephalogram analysis for attention deficit hyperactivity disorder detection using information-theoretic feature selection.

Attention deficit hyperactivity disorder (ADHD) is a neurological disorder that primarily develops in early childhood and affects motor development, v...