Seizures

Patient-Specific Non-Invasive Epileptogenic Zone Localization via High-Resolution Time-Frequency Representations and CNN Deep Learning.

Stereoelectroencephalography (SEEG)-guided radiofrequency thermocoagulation is the mainstream treatment for drug-resistant epilepsy (DRE), yet non-invasive patient-specific localization of potential epileptogenic zone (EZ) prior to SEEG electrode implantation remains a critical unmet clinical need, hindered by limited automation, suboptimal accuracy, and poor cross-patient generalizability. To add...

Distinct tDCS montages act via dissociable lateralization mechanisms to enhance motor function in chronic stroke.

The imbalance in interhemispheric functional connectivity following stroke fundamentally impedes motor recovery. Although transcranial direct current stimulation (tDCS) effectively modulates neuroplasticity, the acute effects of distinct stimulation montages on lateralized brain networks and how these network shifts drive behavioral improvements warrant further investigation. Approach. We emp...

Automated autism spectrum disorder detection using EEG signals and time-frequency visibility graphs.

Early and objective screening of Autism Spectrum Disorder (ASD) remains challenging because conventional diagnosis primarily relies on behavioural ass...

A Novel Adversarial Approach for EEG Dataset Refinement: Enhancing Generalization Through Proximity-to-Boundary Scoring.

As deep learning (DL) performs remarkably in pattern recognition from complex data, it is used to interpret user intentions from electroencephalograph...

Predicting training outcomes for developmental dyslexia from EEG data.

Developmental dyslexia (DD) is characterised by lower-than-average reading abilities and is diagnosed in approximately 10% of individuals. The societa...

"Early Stratification of Risk for Poor Neurological Outcome After Cardiac Arrest Is Improved with Processed EEG Data".

AIM: To evaluate the impact of adding early processed quantitative EEG biomarkers to health record data for early neurological risk stratification aft...

External Validation of EEG-Based Machine Learning Models for Continuous Pain Prediction.

Machine learning (ML) has been used to predict subjective pain intensity from electroencephalographic (EEG) data. However, few ML models for pain asse...

Better sleep now, better cognition later? Predicting cognitive function using a machine learning-based sleep EEG brain health score.

STUDY OBJECTIVES: Sleep state electrocortical activity measured using electroencephalograms (EEG) is linked with cognitive function and dementia risk....

A Novel EEG-Based Topographic Brain Map-Driven Deep Learning Method for Autism Spectrum Disorder Detection in Children.

Autism Spectrum Disorder (ASD) is a neurological and developmental condition that affects children's social and cognitive skills, leading to repetitiv...

A task-aligned multimodal machine learning framework for studying working memory dysfunction in Parkinson's disease.

UNLABELLED: Cognitive impairment is one of the most functionally debilitating non-motor symptoms in Parkinson's disease (PD). Yet, current diagnostic ...

Analyzing Frequency-Space-Time EEG Signatures via Interpretable Neural Networks: A Simulation Study.

OBJECTIVE: Event-related EEG activity is widely investigated to characterize brain functions. Traditional analyses rely on heavy pre-processing and st...

EEG-based classification models reveal differential neural processing of words and images.

BACKGROUND: Machine learning methods employing neuroimaging data are useful for monitoring the activation of neural representations. Specifically, the...

Characterizing transition state in mouse vigilance with electroencephalogram-electromyogram hypnodensity.

STUDY OBJECTIVES: Vigilance-state transitions are continuous biological processes, yet conventional rodent sleep scoring relies on discrete epochs tha...

Prediction of Long-Term Postsurgical Seizure Recurrence From MRI Brain Hub Disruption in Patients With Temporal Lobe Epilepsy.

BACKGROUND AND OBJECTIVES: Patients with temporal lobe epilepsy (TLE) can achieve seizure freedom in the early period after surgery, yet up to half ex...

Comparative evaluation of tabular-to-image deep learning pipelines for EEG-based epileptic seizure recognition: A comprehensive benchmark with statistical analysis and explainable AI.

Epilepsy affects roughly 50 million people worldwide and is diagnosed primarily through electroencephalography (EEG), yet the manual review on which t...

A multi-feature resting-state EEG framework for candidate EEG feature discovery in central vertigo.

Central vertigo (CV) lacks objective electrophysiological measures for severity assessment and rehabilitation monitoring. We aimed to characterize mul...

Time-frequency embedding with contrastive pre-training allows sub-second seizure detection.

OBJECTIVE: Rapid and accurate detection of electrographic seizures is critical for both clinical diagnosis and neuroscience research. Although seizure...

Pharmacological evidence for propagation dynamics of TMS-evoked potentials in the human brain.

BACKGROUND: Transcranial magnetic stimulation-evoked potentials (TEPs) propagate from the stimulation site to distributed brain networks, with early p...

Directed graph neural networks with partial directed coherence for seizure prediction and epileptogenic network characterization.

While electroencephalography (EEG) analyses using undirected connectivity are well-established for seizure prediction and epileptogenic zone (EZ) netw...

Integrating New Approach Methodologies and Artificial Intelligence to Advance Central Nervous System Toxicity Prediction: Lessons from Preclinical and Clinical Case Studies.

Central nervous system (CNS) toxicities remain a major cause of drug attrition and represent a persistent challenge in predicting neurological risk du...