Seizures

Electroencephalogram features support the retrogenesis hypothesis of Alzheimer's disease: Exploratory comparison of brain changes in aging and childhood.

BackgroundThe retrogenesis hypothesis (RH) suggests that the functional and cognitive decline observed in Alzheimer's disease dementia mirrors in reverse order the brain development during childhood and adolescence.ObjectiveEquivalent electroencephalogram (EEG) patterns between older adults across different cognitive decline stages and children across different brain maturation stages were directl...

Difference between eyes-open and eyes-closed resting state quantitative electroencephalography (qEEG) for predicting cognitive impairment using deep learning.

OBJECTIVE: Electroencephalography (EEG) is a method that offers detailed observations of electrical activities occurring in the brain's cerebral cortex. The EEG-derived brain signals can serve as a neurophysiological indicator for the early detection of dementia through quantitative EEG (qEEG) analysis. This study introduces a deep learning (DL)-based classification approach trained using the diff...

γ neuromodulations: unraveling biomarkers for neurological and psychiatric disorders.

γ neuromodulation has emerged as a promising strategy for addressing neurological and psychiatric disorders, particularly in regulating executive and ...

The class imbalance problem in automatic localization of the epileptogenic zone for epilepsy surgery: a systematic review.

Accurate localization of the epileptogenic zone (EZ) is crucial for epilepsy surgery, but the class imbalance of epileptogenic vs. non-epileptogenic e...

A novel dual-branch network for comprehensive spatiotemporal information integration for EEG-based epileptic seizure detection.

Epilepsy is a neurological disorder characterized by recurrent seizures caused by abnormal brain activity, which can severely affects people's normal ...

Epilepsy Prediction via Time-Frequency Features and Multi-Scale Hybrid Neural Networks.

The prediction of epileptic seizures heavily depends on the precise embedding and classification of complex, multi-dimensional electroencephalogram (E...

Cerebral lateralization assessment: an explainable deep learning approach with channel attention mechanism.

In recent years, cross-frequency coupling (CFC) has emerged as a valuable tool in the study of a wide range of cognitive processes due to the strong e...

Advances and Integrations of Computer-Assisted Planning, Artificial Intelligence, and Predictive Modeling Tools for Laser Interstitial Thermal Therapy in Neurosurgical Oncology.

Laser interstitial thermal therapy (LiTT) has emerged as a minimally invasive, MRI-guided treatment of brain tumors that are otherwise considered inop...

Personalizing Responsive Neurostimulation for Epilepsy.

Over the past 20 years, responsive neurostimulation (RNS), a closed-loop device for treating certain forms of drug-resistant focal epilepsy, has becom...

Toward a Universal Map of EEG: A Semantic, Low-Dimensional Manifold for EEG Classification, Clustering, and Prognostication.

OBJECTIVE: Prognostication in patients with disorders of consciousness (DOCs) remains challenging because of heterogeneous etiologies, pathophysiologi...

A New Insight in Cellular and Molecular Signaling Regulation for Neural Differentiation Program.

Numerous neurological conditions impact the brain, spinal cord, and nerves, including neurodegenerative diseases such as Alzheimer's and Parkinson's d...

DynSeizureGAT: Multi-band Dynamic Graph Attention Network for Interpretable Seizure Detection and Analysis of Drug-Resistant Epilepsy Using SEEG.

The dynamic propagation of epileptic discharges complicates Drug-Resistant Epilepsy (DRE) seizure detection using traditional machine learning methods...

Functional connectivity alterations of the pregenual anterior cingulate cortex by ketamine and the modulation by lamotrigine.

BACKGROUND: Neuroimaging studies have linked the beneficial effects of subanaesthetic ketamine doses in psychiatric conditions characterized by chroni...

Super-resolution for localizing electrode grids as small, deformable objects during epilepsy surgery using augmented reality headsets.

PURPOSE: Epilepsy surgery is a potential curative treatment for people with focal epilepsy. Intraoperative electrocorticogram (ioECoG) recordings from...

Seminars in epileptology: How to diagnose status epilepticus in adults and children.

Status epilepticus (SE) can be regarded as the most severe expression of seizure activity characterized by a low probability of spontaneous cessation ...

Test-retest reliability of kinematic and EEG low-beta spectral features in a robot-based arm movement task.

Low-beta (L, 13-20 Hz) power plays a key role in upper-limb motor control and afferent processing, making it a strong candidate for a neurophysiologic...

Identifying and predicting EEG microstates with sequence-to-sequence deep learning models for online applications.

Electroencephalographic (EEG) microstates, as a non-invasive and high-temporal-resolution tool for analyzing time-space features of brain activity, ha...

Impact of brain regions on attention deficit hyperactivity disorder (ADHD) electroencephalogram (EEG) signals: Comparison of machine learning algorithms with empirical mode decomposition and time domain analysis.

OBJECTIVE: This study emphasizes the importance of using proper combinations of brain area, extraction of features, and machine learning (ML) techniqu...

Development and Validation of an Interpretable Machine Learning Model for Predicting Tic Disorders and Severity in Children Based on Electroencephalogram Data.

Accurate diagnosis of Tic disorders (TD) and its severity based on electroencephalogram (EEG) data were of great clinical importance. This study analy...

Early outcome-prediction with an automated EEG background trend in hypothermia-treated newborns with encephalopathy.

BACKGROUND: Therapeutic hypothermia is an intervention that improves outcomes and alters early outcome-prediction in infants with moderate-severe hypo...