Seizures

Resolution of generalized tonic seizures following focal ablative or resective surgery.

OBJECTIVE: Focal brain lesions may underlie generalized tonic seizures, as seen in Lennox-Gastaut syndrome, by engaging bilateral neural networks. However, this seizure type is often not considered surgically remediable. Here, we describe the resolution of apparent electroclinically classic generalized tonic seizures in children originating from a unifocal brain lesion following resective or ablat...

Development and validation of a deep survival model to predict time to seizure from routine electroencephalography.

OBJECTIVE: This study was undertaken to develop and validate a deep survival model (EEGSurvNet) that analyzes routine electroencephalography (EEG) to predict individual seizure risk over time, comparing its performance to traditional clinical predictors such as interictal epileptiform discharges (IEDs). METHODS: We conducted a retrospective cohort study including 1014 consecutive routine EEGs from...

EEGMoE: A Domain-Decoupled Mixture-of-Experts Model for Self-Supervised EEG Representation Learning.

Existing deep learning models for electroencephalogram (EEG) are typically tailored for specific tasks, datasets, or even subjects. This specializatio...

EEG microstate features under visual feedback gain conditions exhibit high sensitivity in identifying early Parkinson's disease patients.

BACKGROUND: Early Parkinson's disease (PD) presents with subtle symptoms and lacks specific diagnostic methods. Clinical diagnosis primarily relies on...

Biomarker discovery in Lennox-Gastaut syndrome: Advances and challenges in electrophysiological, genetic, neuroimaging, and neuroinflammatory approaches.

In Lennox-Gastaut Syndrome (LGS), a severe developmental and epileptic encephalopathy, the absence of validated biomarkers limits our ability to detec...

EEG-to-gait decoding via phase-aware representation learning.

Accurate decoding of lower-limb motion from EEG signals is essential for advancing brain-computer interface (BCI) applications in movement intent reco...

Comparative analysis of machine learning algorithms for predicting stereotactic coordinates of the centromedian nucleus.

OBJECTIVE: Deep brain stimulation (DBS) of the centromedian nucleus (CM) of the thalamus is a promising treatment for drug-resistant epilepsy, Tourett...

Multisequence MRI Enables High-Fidelity FDG-PET Synthesis for Epilepsy Using GANs.

BACKGROUND: FDG-PET aids presurgical epilepsy evaluation but is limited by access and radiation exposure. PURPOSE: To evaluate synthetic FDG-PET gener...

Hybrid quantum classical framework for electroencephalogram driven neurological processing in epileptic seizure taxonomy.

An attention-augmented multimodal classification of alzheimer's disease and parkinson's disease vs healthy controls using MRI, EEG, and SNP data.

Due to the late manifestation of structural symptoms and symptomatic overlap, neurodegenerative diseases such as Parkinson's Disease (PD) and Alzheime...

Enhancing Brain-Computer interface performance through source-level attention mechanism: An EEG motor imagery study.

BACKGROUND: Brain-computer interfaces (BCIs) enable direct communication between humans and machines by translating brain signals into control command...

ACFSENet: an adaptive cross-frequency global sparse encoding network for end-to-end EEG emotion recognition.

End-to-end EEG-based emotion recognition is attracting increasing attention due to its potential in human-computer interaction, mental health, and aff...

Explainable AI for pain perception: subject-independent EEG decoding using DeepSHAP and CNNs.

Objective.Accurate classification of pain levels is essential for clinical monitoring, particularly in clinical populations with limited verbal commun...

Robust multimodal mental workload classification: A cross-physiological condition machine learning approach.

BACKGROUND AND OBJECTIVE: Aircraft pilots can be faced with a high mental workload (MW) combined with moderate hypoxia and sleep restriction. We aimed...

Optimization and validation of multiscale feature selection for EEG-based recognition of drivers' negative emotions.

OBJECTIVE: Negative emotions, such as stress and anger, are significant factors leading to dangerous driving behavior. Investigating the impact of the...

Quantifying the impact of a computer-aided diagnostic score on the clinical diagnosis of functional seizures.

OBJECTIVE: The diagnosis of functional/dissociative seizures (FDS) without ictal video-electroencephalography is challenging. The Functional/Dissociat...

Quantum inspired wavelet and Fourier feature fusion for EEG based epilepsy and seizure detection.

The electroencephalography (EEG) signals are the cheapest approach to study the brain information, commonly used for epilepsy and seizure detection. T...

Rapid, non-invasive diagnosis of tuberculosis using desorption separation ionization mass spectrometry (DSI-MS) and urinary carbonyl metabolite fingerprints.

Tuberculosis (TB) remains a leading global infectious disease that demands rapid, non-invasive diagnostic solutions. Here, we present a rapid urine-ba...

Exploring cognitive workload recognition using CogRepLKNet with EEG-fMRI.

Accurate multimodal Cognitive Workload Recognition (CWR) remains challenging due to the difficulty of modeling cross-modal relationships between Elect...

Unlocking Insights from Postictal EEGs: Investigating Predictive Markers of Seizure Recurrence.

PURPOSE: Seizure recurrence, often presenting as clusters, is a major clinical concern linked to increased morbidity. The immediate postictal period i...