Seizures

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 significant importance. This study aims to explore the neuroelectrophysiological characteristics and diagnostic value of social anxiety by integrating event-related potentials (ERP), event-related spectral perturbation (ERSP), and machine learning method...

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, robust decoding remains a challenge due to the inherent complexity and variability of MI-EEG signals. This study proposes the Temporal Convolutional Attention Network (TCANet), a novel end-to-end model that hierarchically captures spatiotemporal depende...

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...

Automated classification of seizure onset pattern using intracranial electroencephalogram signal of non-human primates.

To develop and validate a machine learning framework for the classification of distinct seizure onset patterns using intracranial EEG (iEEG) recording...

A Comparative Study of Conventional and Tripolar EEG for High-Performance Reach-to-Grasp BCI Systems.

This study aims to enhance brain-computer interface (BCI) applications for individuals with motor impairments by comparing the effectiveness of noninv...

Thalamic neural activity and epileptic network analysis using stereoelectroencephalography: a prospective study protocol.

INTRODUCTION: Epilepsy is a prevalent chronic neurological disorder, with approximately one-third of patients experiencing intractable epilepsy, often...

Dynamic alterations of SEEG characteristics during peri-ictal period and localization of seizure onset zone.

BACKGROUND: The evolution in peri-ictal period (from pre-ictal to ictal phase) of seizures contains abundant epileptogenic information, which aids in ...

Enhancing differentiation between unipolar and bipolar depression through integration of machine learning and electroencephalogram analysis.

To enhance the differentiation between unipolar depression (UPD) and bipolar depression (BPD), this study integrates machine learning and deep learnin...

Optimal graph representations and neural networks for multichannel time series data in seizure phase classification.

In recent years, several machine-learning (ML) solutions have been proposed to solve the problems of seizure detection, seizure phase classification, ...

Attention-driven deep learning framework for EEG analysis in ADHD detection.

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that affects cognitive functions such as attention, impulse control, ...

Resting state EEG classifies developmental status in three-year-old children.

Monitoring cognitive development in early childhood enables detection of problems for timely intervention. However, currently recommended methods requ...

Wearable Epilepsy Seizure Detection on FPGA with Spiking Neural Networks.

The development of epilepsy monitoring solutions suitable for everyday use is a very challenging task, where different constraints should be combined,...

Requirement Analysis for Data-Driven Electroencephalography Seizure Monitoring Software to Enhance Quality and Decision Making in Digital Care Pathways for Epilepsy: A Feasibility Study from the Perspectives of Health Care Professionals.

BACKGROUND: Abnormal brain activity is the source of epileptic seizures, which can present a variety of symptoms and influence patients' quality of li...

Classification of epilepsy seizure types in pediatrics based on Turkish EEG reports.

This study focuses on the binary classification of pediatric epilepsy seizure types as focal or generalized using Turkish electroencephalography (EEG)...

Comprehensive duck DNA fingerprinting based on machine learning for breed identification.

Duck is one of the most widely distributed waterfowl in the world, with more than 6 billion of them farmed annually in the world, and has great econom...

Brain stimulation outcome prediction in Major Depressive Disorder by deep learning models using EEG representations.

Major Depressive Disorder (MDD) is known as a widespread illness and needs a timely treatment. The treatment procedure is currently based on the trial...

An adaptive frequency partitioning framework for epileptic seizure detection using TransseizNet.

OBJECTIVES: Epilepsy is a disorder causing repeated seizures because of unusual brain activity recorded using electroencephalography. Nevertheless, co...

Deep learning on brief interictal intracranial recordings can accurately characterize seizure onset zones.

OBJECTIVE: Epilepsy is a debilitating disorder affecting more than 50 million people worldwide, and one third of patients continue to have seizures de...