Neurology

Seizures

Latest AI and machine learning research in seizures for healthcare professionals.

3,743 articles
Stay Ahead - Weekly Seizures research updates
Subscribe
Browse Specialties
Showing 778-798 of 3,743 articles
Brain-guided manifold transferring to improve the performance of spiking neural networks in image classification.

Spiking neural networks (SNNs), as the third generation of neural networks, are based on biological ...

Deep learning based source imaging provides strong sublobar localization of epileptogenic zone from MEG interictal spikes.

Electromagnetic source imaging (ESI) offers unique capability of imaging brain dynamics for studying...

Machine learning and EEG can classify passive viewing of discrete categories of visual stimuli but not the observation of pain.

Previous studies have demonstrated the potential of machine learning (ML) in classifying physical pa...

Emotion recognition in EEG signals using deep learning methods: A review.

Emotions are a critical aspect of daily life and serve a crucial role in human decision-making, plan...

The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG.

Common misbehavior among children that prevents them from paying attention to tasks and interacting ...

Automatic identification of schizophrenia employing EEG records analyzed with deep learning algorithms.

Electroencephalography is a method of detecting and analyzing electrical activity in the brain. This...

A novel framework for classification of two-class motor imagery EEG signals using logistic regression classification algorithm.

Robotics and artificial intelligence have played a significant role in developing assistive technolo...

Deep learning for automated detection of generalized paroxysmal fast activity in Lennox-Gastaut syndrome.

OBJECTIVES: Generalized paroxysmal fast activity (GPFA) is a key electroencephalographic (EEG) featu...

The influence of EEG channels and features significance on automatic detection of epileptic waves in MECT.

Modified Electric Convulsive Therapy (MECT) is an efficacious physical therapy in treating mental di...

ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study.

Mental health, especially stress, plays a crucial role in the quality of life. During different phas...

An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea.

Automatic deep-learning models used for sleep scoring in children with obstructive sleep apnea (OSA)...

Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning.

"Attention-Deficit Hyperactivity Disorder (ADHD)" is a neuro-developmental disorder in children unde...

An Explainable EEG-Based Human Activity Recognition Model Using Machine-Learning Approach and LIME.

Electroencephalography (EEG) is a non-invasive method employed to discern human behaviors by monitor...

Review of Performance Improvement of a Noninvasive Brain-computer Interface in Communication and Motor Control for Clinical Applications.

Brain-computer interfaces (BCI) enable direct communication between the brain and a computer or othe...

An artificial intelligence-based pipeline for automated detection and localisation of epileptic sources from magnetoencephalography.

Magnetoencephalography (MEG) is a powerful non-invasive diagnostic modality for presurgical epilepsy...

Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy.

OBJECTIVE: This study aimed to explore sensitive detection methods for pathological high-frequency o...

Decoding movement kinematics from EEG using an interpretable convolutional neural network.

Continuous decoding of hand kinematics has been recently explored for the intuitive control of elect...

A novel method for modeling effective connections between brain regions based on EEG signals and graph neural networks for motor imagery detection.

Classified as biomedical signal processing, cerebral signal processing plays a key role in human-com...

Self-Attentive Channel-Connectivity Capsule Network for EEG-Based Driving Fatigue Detection.

Deep neural networks have recently been successfully extended to EEG-based driving fatigue detection...

Memristive Neural Networks for Predicting Seizure Activity.

UNLABELLED: is to assess the possibilities of predicting epileptiform activity using the neuronal a...

Browse Specialties