Seizures

Can Artificial Intelligence Chatbots Plan Therapeutic Ketogenic Diets for Children With Epilepsy?

RATIONALE: Ketogenic Diet Therapy (KDT) is an effective but complex treatment for paediatric drug-resistant epilepsy. Access to trained dietitians limits the global use of KDT. The increasing use of artificial intelligence (AI) chatbots for health and dietary advice presents both opportunities and risks. This exploratory study evaluates the accuracy and feasibility of AI-generated KDT plans for pa...

A novel and accurate EEG emotion classification model based on multiple attention local binary patterns.

BACKGROUND: Electroencephalography (EEG) signals play a crucial role in understanding brain activity because they provide useful information about real emotions and intentions. Many machine learning models have been used for automatic EEG-based emotion classification. However, previous studies remain limited by restricted feature representations and insufficient subject-independent validation. MET...

EEG monitoring in the operating room: current uses and perspectives - a narrative review.

CONTEXT AND IMPORTANCE: With over 300 million surgeries performed under general anaesthesia annually, optimising perioperative brain health has become...

Analysis of electroencephalogram microstates and spectral power in children and adolescents with attention deficit hyperactivity disorder.

Attention-Deficit/Hyperactivity Disorder (ADHD) is a widely recognized neurodevelopmental disorder characterized by inattention, hyperactivity, and im...

Beyond Broca and Wernicke: Epilepsy surgery in the language areas.

Epilepsy surgery in language areas is challenged by the intricacies of presurgical workup and surgical planning. In recent decades, the view of langua...

EEG-based dataset explicitly targets the transitions between sitting and standing for exploring neural activation patterns in Motor Imagery and execution.

This study presents the first publicly accessible electroencephalography (EEG) dataset explicitly targeting sit-to-stand and stand-to-sit transitions ...

Multi-scale dynamic cooperative attention network for Auditory Attention Detection.

Humans exhibit a remarkable capacity to concentrate on particular auditory inputs amid multiple simultaneous speakers, as seen in cocktail party setti...

EEG biomarkers can predict early-stage Alzheimer's disease and correlate with intracerebral pathology: a multimodal machine learning study.

BACKGROUND: Early recognition of Alzheimer's disease (AD) is crucial for timely intervention and delaying disease progression. Electroencephalogram (E...

Different pattern: a new EEG-based method for mental performance detection.

BACKGROUND: This study focuses on detecting mental performance from EEG signals. It provides both classification and explanation results. For this pur...

Integrating multidimensional nociceptive-related cortical features for unsupervised assessment of anesthesia states in rats.

Accurate anesthesia monitoring remains challenging because current approaches primarily assess consciousness while overlooking nociceptive processing....

Epilepsy seizure prediction based on ViT-2DCNN spatio-temporal fusion model.

OBJECTIVE: Epilepsy is a chronic neurological disorder characterized by recurrent and sudden seizures. Accurate prediction of epileptic seizures holds...

EEG Biomarkers of Accelerated Prolonged Intermittent Theta-Burst Stimulation in Depression: Machine Learning with a Minimal-Electrode Montage.

Frequency- and Network-Specific Changes in Functional Connectivity Reflect Pathophysiological Mechanisms across Parkinson's Disease Stages.

OBJECTIVE: Parkinson's disease (PD) is increasingly conceptualized as a disorder of large-scale brain networks, yet whether and how frequency-specific...

Comparative machine learning for accurate EEG-based epileptic seizure state classification using sub-band analysis.

Timely identification of seizure-related EEG states can support clinical assessment and motivate future monitoring tools. This study investigates a co...

Hybrid EEG Feature Fusion Framework for Accurate Autism Spectrum Disorder Diagnosis Using Ensemble Learning.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition with increasing global prevalence and no standardized biological test for ear...

Optimization of attention mechanisms in choral conducting education using EEG and eye movement tracking with transfer learning algorithms.

This study presents a comprehensive investigation into attention mechanism optimization in choral conducting education through the integration of elec...

Prediction of Neurological Outcome and Mortality in Cardiac Arrest Patients: An Explainable Machine Learning Study Integrating HRV, EEG, and Clinical Features.

OBJECTIVE: Early and accurate prediction of neurological outcomes and mortality in comatose patients after cardiac arrest remains challenging. Multimo...

Towards generalizable seizure monitoring: EpiVLM for cross-environment detection and classification.

The translation of automated seizure detection from controlled clinical units to real-world settings is hindered by heterogeneous recording conditions...

Genetic algorithm-optimized machine learning approaches for EEG-based silent speech decoding.

The phases of human communication consist of speech perception, production, and imagination. The objective of this work is to understand and analyse t...

Lightweight deep learning model for nonconvulsive status epilepticus diagnosis using EEG time-frequency analysis.

Nonconvulsive Status Epilepticus (NCSE) is a persistent epileptic seizure state whose detection primarily relies on visual EEG inspection. Automated a...