Seizures

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 purpose, we developed a new feature extraction method called Different Pattern (DiffPat) within an Explainable Feature Engineering (XFE) framework. NEW METHOD: The proposed approach utilizes an EEG mental performance dataset. The DiffPat algorithm extra...

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. Although electroencephalography (EEG)-based metrics such as permutation entropy (PE) and permutation cross-mutual information (PCMI) are widely used, nociceptive-evoked cortical responses, especially gamma-band oscillations (GBOs), a robust index of...

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.

Predicting the antifungal effects of ethanolic propolis and geopropolis extracts on Aspergillus flavus growth using mechanistic and machine-learning models.

This study evaluated the antifungal potential of ethanolic propolis (EEP) and geopropolis (EEG) extracts against Aspergillus flavus, integrating mecha...

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...

Clinical and EEG features integrated by a deep learning method to investigate treatment outcomes in epilepsy with eyelid myoclonia.

OBJECTIVE: To explore the correlation between clinical and electroencephalogram (EEG) factors and therapeutic outcome in epilepsy with eyelid myocloni...

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...

Developmental Profile of Physiological High-Frequency Oscillations in the Human Brain.

High-frequency oscillations (HFOs), transient burst of ≥ 80 Hz activity, are increasingly recognized as promising EEG biomarkers of the epileptogenic ...

EEG microstate dynamics and metal metabolism in Parkinson's disease dementia: an exploratory study with machine learning-assisted sensitivity analysis.

BACKGROUND: Electroencephalogram (EEG) microstates effectively characterise cognitive-related brain networks, and metal homeostasis is crucial for mai...

Isolating Eye-Movement Artifacts from EEG Signals.

The electroencephalogram (EEG) provides a direct measure of brain electrical activity but is typically contaminated by artifacts, most notably those a...

Prediction and diagnosis of post-stroke epilepsy using artificial intelligence approaches: a systematic review and meta-analysis.

INTRODUCTION: Post-stroke epilepsy (PSE) is a common complication following a stroke and is a major cause of epilepsy in the elderly. Artificial intel...

AI-Integrated EEG Decision Support for Neurocritical Care: A Conceptual and Feasibility Framework.

The clinical use of electroencephalography (EEG) for neuro-prognostication in neurocritical care remains limited, despite its ability to provide non-i...

Spatio-Temporal Attention with Spiking Neural Networks for Seizure Detection from Electroencephalogram Signals.

Timely and accurate detection of seizures from Electroencephalogram (EEG) signals is critical for the effective management of epilepsy. Although deep ...