Seizures

Crucial rhythms and subnetworks for emotion processing extracted by an interpretable deep learning framework from EEG networks.

Electroencephalogram (EEG) brain networks describe the driving and synchronous relationships among multiple brain regions and can be used to identify different emotional states. However, methods for extracting interpretable structural features from brain networks are still lacking. In the current study, a novel deep learning structure comprising both an attention mechanism and a domain adversarial...

Topological analysis of brain dynamical signals indicates signatures of seizure susceptibility

Epilepsy is known to drastically alter brain dynamics during seizures (ictal periods), but its effects on background (non-ictal) brain dynamics remain poorly understood. To investigate this, we analyzed an in-house dataset of brain activity recordings from epileptic zebrafish, focusing on two controlled genetic conditions across two fishlines. After using machine learning to segment and label re...

Prediction of Post Traumatic Epilepsy Using MR-Based Imaging Markers.

Post-traumatic epilepsy (PTE) is a debilitating neurological disorder that develops after traumatic brain injury (TBI). Despite the high prevalence of...

Knowledge-Data Fusion Based Source-Free Semi-Supervised Domain Adaptation for Seizure Subtype Classification

Electroencephalogram (EEG)-based seizure subtype classification enhances clinical diagnosis efficiency. Source-free semi-supervised domain adaptatio...

Protecting Multiple Types of Privacy Simultaneously in EEG-based Brain-Computer Interfaces

A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is the preferred ...

Towards Personalized Brain-Computer Interface Application Based on Endogenous EEG Paradigms

In this paper, we propose a conceptual framework for personalized brain-computer interface (BCI) applications, which can offer an enhanced user expe...

A Multi-Label EEG Dataset for Mental Attention State Classification in Online Learning

Attention is a vital cognitive process in the learning and memory environment, particularly in the context of online learning. Traditional methods f...

EEG Spectral Analysis in Gray Zone Between Healthy and Insomnia

This study investigates the sleep characteristics and brain activity of individuals in the gray zone of insomnia, a population that experiences slee...

A Feature Fusion Model Based on Temporal Convolutional Network for Automatic Sleep Staging Using Single-Channel EEG.

Sleep staging is a crucial task in sleep monitoring and diagnosis, but clinical sleep staging is both time-consuming and subjective. In this study, we...

Real-time Sub-milliwatt Epilepsy Detection Implemented on a Spiking Neural Network Edge Inference Processor

Analyzing electroencephalogram (EEG) signals to detect the epileptic seizure status of a subject presents a challenge to existing technologies aimed...

EEG-estimated functional connectivity, and not behavior, differentiates Parkinson's patients from health controls during the Simon conflict task

Neural biomarkers that can classify or predict disease are of broad interest to the neurological and psychiatric communities. Such biomarkers can be...

The Effect of Acute Stress on the Interpretability and Generalization of Schizophrenia Predictive Machine Learning Models

Introduction Schizophrenia is a severe mental disorder, and early diagnosis is key to improving outcomes. Its complexity makes predicting onset and ...

Seizure freedom after surgical resection of diffusion-weighted MRI abnormalities

Importance: Many individuals with drug-resistant epilepsy continue to have seizures after resective surgery. Accurate identification of focal brain ...

From Epilepsy Seizures Classification to Detection: A Deep Learning-based Approach for Raw EEG Signals

Epilepsy represents the most prevalent neurological disease in the world. One-third of people suffering from mesial temporal lobe epilepsy (MTLE) ex...

NECOMIMI: Neural-Cognitive Multimodal EEG-informed Image Generation with Diffusion Models

NECOMIMI (NEural-COgnitive MultImodal EEG-Informed Image Generation with Diffusion Models) introduces a novel framework for generating images direct...

Feature Estimation of Global Language Processing in EEG Using Attention Maps

Understanding the correlation between EEG features and cognitive tasks is crucial for elucidating brain function. Brain activity synchronizes during...

A Survey of Spatio-Temporal EEG data Analysis: from Models to Applications

In recent years, the field of electroencephalography (EEG) analysis has witnessed remarkable advancements, driven by the integration of machine lear...

Towards Explainable Graph Neural Networks for Neurological Evaluation on EEG Signals

After an acute stroke, accurately estimating stroke severity is crucial for healthcare professionals to effectively manage patient's treatment. Grap...

Differentially Private Multimodal Laplacian Dropout (DP-MLD) for EEG Representative Learning

Recently, multimodal electroencephalogram (EEG) learning has shown great promise in disease detection. At the same time, ensuring privacy in clinica...

[An autoencoder model based on one-dimensional neural network for epileptic EEG anomaly detection].

OBJECTIVE: We propose an autoencoder model based on a one-dimensional convolutional neural network (1DCNN) as the feature extraction network for effic...