Seizures

Accuracy of Machine Learning Algorithms Based on Electroencephalogram in Sleep Apnea Detection: Systematic Review and Meta-Analysis.

BACKGROUND: Sleep apnea (SA) is a serious sleep disorder, and its diagnostic gold standard, polysomnography, is costly and time-consuming. Electroencephalogram (EEG) signals, due to their direct correlation with neural activity and ease of extraction, represent a promising tool. Despite increasing research on machine learning (ML) and deep learning for EEG-based SA detection, model performance has...

Deep learning architectures for EEG-based classification of Dravet syndrome: A comparative study of pre-trained and non-pretrained hybrid CNN-LSTM models.

OBJECTIVE: This study explores the potential of artificial intelligence (AI) using a hybrid deep learning Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) framework, for EEG-based classification and analysis of Dravet Syndrome (DS). METHOD: The study cohort comprised nine pediatric patients with DS, confirmed through either a heterozygous pathogenic mutation in the SCN1A gene or a cl...

BFRCNet: addressing the class imbalance problem in the rapid serial visual presentation paradigm for decoding.

Objective.Imbalanced sample sizes in rapid serial visual presentation (RSVP) can substantially compromise the classification accuracy of electroenceph...

Psychoneuroimmunology-informed strategies for early detection of biological threat exposure: Insights from experimental rodent models.

The growing frequency of emerging infectious diseases, antimicrobial resistance, and accidental or deliberate biological threat releases underscores t...

A multi-paradigm and longitudinal EEG dataset including the "sixth-finger" and "affected-hand" motor imagery of stroke patients.

Motor imagery-based brain-computer interface (MI-BCI) applications in stroke rehabilitation aim to match brain activity with real-time feedback, there...

The Use of Artificial Intelligence in Neonatal Seizure Detection: An Artificial Intelligence-Assisted Systematic Review.

BACKGROUND: Artificial intelligence (AI) is increasingly used in health care. We systematically reviewed evidence on the accuracy of AI in detecting n...

Subthalamic spatio-spectral-connectivity of psychiatric symptoms in Parkinson's disease.

Psychiatric symptoms in Parkinson's disease (PD) are highly prevalent and challenging to treat. This study maps oscillatory neural activity to diverse...

Bridging subjective and neural state transitions in the rubber hand illusion: a neurophenomenological study.

The rubber hand illusion (RHI) provides a powerful paradigm for probing the malleability of bodily self-consciousness. While conventional studies rely...

HFG-Net: High-frequency guided multi-view graph convolution and dynamic spatio-temporal fusion for ASD diagnosis.

Autism Spectrum Disorder (ASD) is a highly heterogeneous neurodevelopmental condition characterized by significant inter-subject variability in electr...

Interpretable Dual-Stream EEG-MRI Fusion Uncovers Structure-Function Signatures of Stroke Motor Recovery.

Motor recovery prediction after stroke is hindered by the inability of single-modality imaging to capture how structural damage and functional reorgan...

NeuroOmics-Net: An interpretable multimodal deep learning framework for Alzheimer's disease diagnosis and progression prediction using neuroimaging, EEG, and genomic data.

Accurate diagnosis and progression prediction of Alzheimer's disease (AD) remain challenging due to the heterogeneous nature of the disease, which inv...

Photosensitivity in Lafora and Unverricht-Lundborg progressive myoclonic epilepsies.

OBJECTIVE: Lafora disease (LD) and Unverricht-Lundborg disease (EPM1A) are the most common forms of progressive myoclonic epilepsy and are frequently ...

The effectiveness of adding on or switching antiseizure medications after the first fails to control focal epilepsy: A systematic review of randomized controlled trials.

Focal epilepsy constitutes 60-70% of epilepsy, and up to half of patients do not achieve seizure freedom with their first antiseizure medication (ASM)...

Beyond FDG: a paradigm shift in precision localization of epileptogenic zones in refractory epilepsy using multimodal molecular imaging (PET/CT) and isotropic 3D MRI fusion.

Surgical resection for drug-resistant focal epilepsy relies on the precise presurgical localization of the epileptogenic zone (EZ). Although [1⁸F]FDG-...

A Framework for Predicting Neurofeedback Treatment Response in ADHD Using EEG Functional Connectivity and Genetic Algorithm-Driven Channel Selection.

In this article, we present a computational framework for predicting treatment response to neurofeedback (NF) among patients with Attention-Deficit/Hy...

Unmasking data leakage in EEG-ADHD literature: a rigorous, interpretable SOTA framework (DSAEN).

The field of translational Electroencephalogram-Artificial Intelligence (EEG-AI) faces a significant methodological challenge regarding epoch-wise dat...

EEG-based automated evaluation of automotive sound quality using ensemble deep learning.

The evaluation of automotive sound quality is of considerable significance for improving driving comfort. However, existing methodologies suffer from ...

The underlying mechanisms of tDCS-Based cognitive enhancement in unpredictable task switching: A machine learning-based approach for EEG signal analysis.

Transcranial direct current stimulation (tDCS) enhances cognitive abilities yet has highly inconsistent outcomes, highlighting the need to clarify its...

Machine learning approaches for prediction of epilepsy risk across clinical pathways: a systematic review.

Machine learning (ML) and deep learning (DL) models are increasingly being explored for individualized epilepsy risk prediction after a first unpr...

Diagnosis of Neurological Dysfunction from EEG Signals Using DuelQ-SeizureNet and Meta Black Ant Optimization for Epileptic Seizure Detection.

BACKGROUND: Early diagnosis of neurological dysfunctions, particularly epilepsy, is vital for early intervention and improvement of patients' quality ...