Seizures

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 (EEG) signals. Due to individual variability and the dynamic non-linear nature of EEG signals, extracting highly discriminative spatiotemporal features is a core challenge in this field. In this study, to address this issue, we proposed a novel archite...

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 evidence of its functional role in neural computation and communication. CFC computed from electroencephalography (EEG) signals provides powerful information for detecting certain neurological conditions associated with atypical cerebral lateralizatio...

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...

Analysis of the neural mechanisms of social anxiety based on EEG features and machine learning and construction of a diagnostic model.

Social anxiety is a common psychological problem, and its accurate diagnosis and investigation of underlying neurophysiological mechanisms are of sign...

TCANet: a temporal convolutional attention network for motor imagery EEG decoding.

Decoding motor imagery electroencephalogram (MI-EEG) signals is fundamental to the development of brain-computer interface (BCI) systems. However, rob...

Recent Advances in sMRI and Artificial Intelligence for Presurgical Planning in Focal Cortical Dysplasia: A Systematic Review.

BACKGROUND: Focal Cortical Dysplasia (FCD) is a leading cause of drug-resistant epilepsy, particularly in children and young adults, necessitating pre...

Fusion of FDG and FMZ PET Reduces False-Positives in Predicting Epileptogenic Zone.

BACKGROUND AND PURPOSE: Epilepsy, a globally prevalent neurologic disorder, necessitates precise identification of the epileptogenic zone (EZ) for eff...

POC-CSP: a novel parameterised and orthogonally-constrained neural network layer for learning common spatial patterns (CSP) in EEG signals.

. Common spatial patterns (CSPs) has been established as a powerful feature extraction method in EEG signal processing with machine learning, but it h...