Seizures

Raw EEG as a viable alternative to engineered decompositions in anesthetic depth prediction.

PURPOSE: Assessing the depth of anesthesia remains a challenge in operating rooms worldwide, as hospitals often rely on proprietary monitors that are costly and inaccessible to low-resource institutions. This research explores whether machine learning can predict indicators of anesthetic depth from intraoperative EEG, and whether established preprocessing methods significantly improve performance....

Robust multi-centre interictal EEG biomarker for distinguishing epilepsy from mimickers.

OBJECTIVE: To develop and validate an interpretable multi-centre interictal EEG biomarker for distinguishing epilepsy from mimickers, addressing the critical gap of single-centre studies with limited sample sizes in prior literature. METHODS: We analysed routine interictal EEG from 448 subjects across two tertiary centres (IHBAS: N = 230; MAX: N = 218), encompassing diverse epilepsy subtypes and m...

Prediction of an fMRI-based schizophrenia biomarker from EEG using dynamic functional connectivity: a simultaneous EEG-fMRI study.

OBJECTIVE: Recent advances in functional magnetic resonance imaging (fMRI) have identified brain functions associated with psychiatric disorders using...

Resting-state EEG microstates as biomarkers for major depressive disorder.

OBJECTIVES: To investigate the temporal dynamics of resting-state electroencephalography (EEG) microstates in patients with Major Depressive Disorder ...

EEG-based dynamic emotion recognition using multi-scale wavelet transform with a Spatio-Temporal neural network.

Emotion recognition from EEG signals has been one of the most promising areas due to its potential in enhancing human-computer interaction, especially...

Neural Response to Familiar Names Predicts Outcome of Comatose ICU Patients: A Prospective Observational Cohort Study.

Predicting the outcome of comatose patients in the intensive care unit (ICU) can inform decision making but remains challenging. Recent studies sugges...

Comparison of volume- and surface-based magnetic resonance imaging morphometry algorithms in the detection of focal cortical dysplasia.

OBJECTIVE: Detection of focal cortical dysplasia (FCD) remains a major challenge in presurgical epilepsy diagnostics. Magnetic resonance imaging (MRI)...

A Hybrid Deep Learning Framework with Supervised Contrastive Learning for Robust Seizure Detection in Long-Term EEG.

Accurate and timely automatic detection of epileptic seizures is crucial for reducing the workload for visually inspecting long-term electroencephalog...

TRIDENT: A multi-task, triple-branch deep learning framework for EEG-based recognition, severity estimation, and future high-anger prediction in an on-road Wizard-of-Oz paradigm.

Driving anger is strongly associated with aggressive driving and elevated crash risk. However, continuous modeling of graded anger-related affective s...

Integrating multisequence radiomics and clinical features to predict seizure recurrence after gross total resection of pediatric low-grade epilepsy-associated brain tumors.

OBJECTIVE: This study aimed to develop a predictive model integrating clinical features and multisequence MRI radiomics to forecast postoperative seiz...

Synaptic dysfunction-related gene expression spatially correlates with widespread cortical network atrophy in temporal lobe epilepsy.

Temporal lobe epilepsy (TLE) exhibits marked lateralized gray matter alterations, yet whole-brain network vulnerability patterns, particularly those i...

Evaluating the biological meaning of neural network decisions in EEG-based MCI detection.

OBJECTIVE: High accuracy in medical classification tasks does not ensure that neural networks reason in ways consistent with clinical or neurobiologic...

High-frequency EEG synchronization modes as a stable biometric signature in humans.

Electroencephalography (EEG) offers a promising modality for biometric identification, though balancing performance, interpretability, and robustness ...

Point-of-Care Artificial Intelligence Measure of Seizure Burden Associates With Clinical Outcome at Discharge.

OBJECTIVES: Point-of-care (POC) electroencephalography (EEG) enabled with artificial intelligence (AI) algorithms hold the potential to address gaps i...

Machine learning models for predicting postpartum convulsions using clinical indicators from PMA Ethiopia data.

Postpartum convulsions, defined as seizure episodes occurring after childbirth during the postpartum period, remain a major cause of maternal morbidit...

Characterizing Central-Autonomic Dynamics During an Episodic Memory Task Using Multi-Modal Neural and Cardiomechanical Signals.

OBJECTIVE: Memory function underlies mental and behavioral health. While the role of the central nervous system (CNS) during episodic memory encoding ...

Cross-Hemispheric Spatial-Temporal Attention Network for Decoding Silent Speech From EEG.

OBJECTIVE: Speech, as the core of advanced human cognition, is fundamental to social interaction and daily life. Electroencephalogram (EEG)-based spee...

A Hierarchical Multimodal Framework for Sedation Monitoring in ICU Patients.

In the intensive care unit (ICU), monitoring sedation levels is crucial. Clinicians often rely on intermittent behavioral scales like the Richmond Agi...

An Active Dry-Contact Continuous EEG Monitoring System for Seizure Detection Applications in Clinical Neurophysiology.

OBJECTIVE: Young children and infants, especially newborns, are highly susceptible to seizures, which, if undetected and untreated, can lead to severe...

BioGAP-Ultra: A Modular Edge-AI Platform for Wearable Multimodal Biosignal Acquisition and Processing.

The growing demand for continuous physiological monitoring and human-machine interaction in real-world settings calls for wearable platforms that are ...