Neurology

Seizures

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

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Bridging Neuroscience and Robotics: Spiking Neural Networks in Action.

Robots are becoming increasingly sophisticated in the execution of complex tasks. However, an area t...

Menthol Dissolved in Dimethyl Sulfoxide Protects Against Epileptiform Activity Induced by Pentylenetetrazol in Male Rats.

INTRODUCTION: This research aims to investigate the protective action of menthol dissolved in dimeth...

Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning.

Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering bo...

Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms.

The aim of this study was to develop machine learning classification models using electroencephalogr...

An empirical comparison of deep learning explainability approaches for EEG using simulated ground truth.

Recent advancements in machine learning and deep learning (DL) based neural decoders have significan...

Characterisation of Cognitive Load Using Machine Learning Classifiers of Electroencephalogram Data.

A high cognitive load can overload a person, potentially resulting in catastrophic accidents. It is ...

Natural language processing for identification of refractory status epilepticus in children.

OBJECTIVE: Pediatric status epilepticus is one of the most frequent pediatric emergencies, with high...

Detection and classification of adult epilepsy using hybrid deep learning approach.

The electroencephalogram (EEG) has emerged over the past few decades as one of the key tools used by...

Brain-computer interface for robot control with eye artifacts for assistive applications.

Human-robot interaction is a rapidly developing field and robots have been taking more active roles ...

Deep Learning-Based Classification of Epileptic Electroencephalography Signals Using a Concentrated Time-Frequency Approach.

ConceFT (concentration of frequency and time) is a new time-frequency (TF) analysis method which com...

Electroencephalographic abnormalities in children with type 1 diabetes mellitus: a prospective study.

BACKGROUND/AIM: The aim herein was to investigate epileptiform discharges on electroencephalogram (E...

Evaluation of a novel real-time adaptive assist-as-needed controller for robot-assisted upper extremity rehabilitation following stroke.

Rehabilitation therapy plays an essential role in assisting people with stroke regain arm function. ...

Bayesian learning from multi-way EEG feedback for robot navigation and target identification.

Many brain-computer interfaces require a high mental workload. Recent research has shown that this c...

EEG-Based Cross-Subject Driver Drowsiness Recognition With an Interpretable Convolutional Neural Network.

In the context of electroencephalogram (EEG)-based driver drowsiness recognition, it is still challe...

Spectral analysis and Bi-LSTM deep network-based approach in detection of mild cognitive impairment from electroencephalography signals.

Mild cognitive impairment (MCI) is a neuropsychological syndrome that is characterized by cognitive ...

Deep Learning for Detecting Multi-Level Driver Fatigue Using Physiological Signals: A Comprehensive Approach.

A large share of traffic accidents is related to driver fatigue. In recent years, many studies have ...

An ensemble deep-learning approach for single-trial EEG classification of vibration intensity.

. Single-trial electroencephalography (EEG) classification is a promising approach to evaluate the c...

Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia.

(SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations,...

Applications for Deep Learning in Epilepsy Genetic Research.

Epilepsy is a group of brain disorders characterised by an enduring predisposition to generate unpro...

A Deep Learning Model for Correlation Analysis between Electroencephalography Signal and Speech Stimuli.

In recent years, the use of electroencephalography (EEG) has grown as a tool for diagnostic and brai...

Overview of methods and available tools used in complex brain disorders.

Complex brain disorders, including Alzheimer's dementia, sleep disorders, and epilepsy, are chronic ...

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