Seizures

Deep learning-based stress detection for daily life use using single-channel EEG and GSR in a virtual reality interview paradigm.

This research aims to establish a practical stress detection framework by integrating physiological ...

Unraveling Brain Synchronisation Dynamics by Explainable Neural Networks using EEG Signals: Application to Dyslexia Diagnosis.

The electrical activity of the neural processes involved in cognitive functions is captured in EEG s...

A Method to Extract Task-Related EEG Feature Based on Lightweight Convolutional Neural Network.

Unlocking task-related EEG spectra is crucial for neuroscience. Traditional convolutional neural net...

Power spectral density-based resting-state EEG classification of first-episode psychosis.

Historically, the analysis of stimulus-dependent time-frequency patterns has been the cornerstone of...

SMARTSeiz: Deep Learning With Attention Mechanism for Accurate Seizure Recognition in IoT Healthcare Devices.

The Internet of Things (IoT) is capable of controlling the healthcare monitoring system for remote-b...

MASA-TCN: Multi-Anchor Space-Aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition.

Emotion recognition from electroencephalogram (EEG) signals is a critical domain in biomedical resea...

A Deep Learning Approach to Estimate Multi-Level Mental Stress From EEG Using Serious Games.

Stress is revealed by the inability of individuals to cope with their environment, which is frequent...

Unsupervised and semi-supervised domain adaptation networks considering both global knowledge and prototype-based local class information for Motor Imagery Classification.

The non-stationarity of EEG signals results in variability across sessions, impeding model building ...

Efficient Generalized Electroencephalography-Based Drowsiness Detection Approach with Minimal Electrodes.

Drowsiness is a main factor for various costly defects, even fatal accidents in areas such as constr...

MSE-VGG: A Novel Deep Learning Approach Based on EEG for Rapid Ischemic Stroke Detection.

Ischemic stroke is a type of brain dysfunction caused by pathological changes in the blood vessels o...

Development and validation of an interpretable machine learning model for predicting post-stroke epilepsy.

BACKGROUND: Epilepsy is a serious complication after an ischemic stroke. Although two studies have d...

Wavelet Transform, Reconstructed Phase Space, and Deep Learning Neural Networks for EEG-Based Schizophrenia Detection.

This study proposes an innovative expert system that uses exclusively EEG signals to diagnose schizo...

An rs-fMRI based neuroimaging marker for adult absence epilepsy.

OBJECTIVE: Approximately 20-30 % of epilepsy patients exhibit negative findings on routine magnetic ...

HiRENet: Novel convolutional neural network architecture using Hilbert-transformed and raw electroencephalogram (EEG) for subject-independent emotion classification.

BACKGROUND AND OBJECTIVES: Convolutional neural networks (CNNs) are the most widely used deep-learni...

When performing actions with robots, attribution of intentionality affects the sense of joint agency.

Sense of joint agency (SoJA) is the sense of control experienced by humans when acting with others t...

Prediction of Robotic Anastomosis Competency Evaluation (RACE) metrics during vesico-urethral anastomosis using electroencephalography, eye-tracking, and machine learning.

Residents learn the vesico-urethral anastomosis (VUA), a key step in robot-assisted radical prostate...

Robots as Mental Health Coaches: A Study of Emotional Responses to Technology-Assisted Stress Management Tasks Using Physiological Signals.

The current study investigated the effectiveness of social robots in facilitating stress management ...

A machine learning approach for differentiating bipolar disorder type II and borderline personality disorder using electroencephalography and cognitive abnormalities.

This study addresses the challenge of differentiating between bipolar disorder II (BD II) and border...

Validation of artificial intelligence for epileptic seizure detection: Moving from research into the clinic.

Multiclass Classification of Visual Electroencephalogram Based on Channel Selection, Minimum Norm Estimation Algorithm, and Deep Network Architectures.

This work addresses the challenge of classifying multiclass visual EEG signals into 40 classes for b...

SleepFC: Feature Pyramid and Cross-Scale Context Learning for Sleep Staging.

Automated sleep staging is essential to assess sleep quality and treat sleep disorders, so the issue...