AIMC Topic: Electroencephalography

Clear Filters Showing 661 to 670 of 2121 articles

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

Sensors (Basel, Switzerland) Oct 17, 2023
A high cognitive load can overload a person, potentially resulting in catastrophic accidents. It is therefore important to ensure the level of cognitive load associated with safety-critical tasks (such as driving a vehicle) remains manageable for dri...
Electroencephalography Cognition Support Vector Machine Humans Machine Learning Computer Simulation Neural Networks, Computer
View on PubMed DOI

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

Scientific reports Oct 16, 2023
The electroencephalogram (EEG) has emerged over the past few decades as one of the key tools used by clinicians to detect seizures and other neurological abnormalities of the human brain. The proper diagnosis of epilepsy is crucial due to its distinc...
Humans Neural Networks, Computer Electroencephalography Epilepsy Signal Processing, Computer-Assisted Deep Learning Seizures Algorithms Child Brain
View on PubMed DOI

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

Scientific reports Oct 16, 2023
Human-robot interaction is a rapidly developing field and robots have been taking more active roles in our daily lives. Patient care is one of the fields in which robots are becoming more present, especially for people with disabilities. People with ...
User-Computer Interface Robotics Electroencephalography Algorithms Humans Eye Movements Artifacts Brain-Computer Interfaces
View on PubMed DOI

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

International journal of neural systems Oct 13, 2023
ConceFT (concentration of frequency and time) is a new time-frequency (TF) analysis method which combines multitaper technique and synchrosqueezing transform (SST). This combination produces highly concentrated TF representations with approximately p...
Seizures Signal Processing, Computer-Assisted Algorithms Brain Electroencephalography Epilepsy Humans Deep Learning
View on PubMed DOI

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

Turkish journal of medical sciences Oct 12, 2023
BACKGROUND/AIM: The aim herein was to investigate epileptiform discharges on electroencephalogram (EEG), their correlation with glutamic acid decarboxylase 65 autoantibody (GAD-ab) in newly diagnosed pediatric type 1 diabetes mellitus (T1DM) patients...
Epilepsy Autoantibodies Glutamate Decarboxylase Longitudinal Studies Electroencephalography Infant Female Adolescent Male Child Child, Preschool Humans Diabetes Mellitus, Type 1 Prospective Studies
View on PubMed DOI

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

Scientific reports Oct 7, 2023
Many brain-computer interfaces require a high mental workload. Recent research has shown that this could be greatly alleviated through machine learning, inferring user intentions via reactive brain responses. These signals are generated spontaneously...
Brain Robotics Bayes Theorem Neurofeedback Electroencephalography Brain-Computer Interfaces
View on PubMed DOI

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

IEEE transactions on neural networks and learning systems Oct 5, 2023
In the context of electroencephalogram (EEG)-based driver drowsiness recognition, it is still challenging to design a calibration-free system, since EEG signals vary significantly among different subjects and recording sessions. Many efforts have bee...
Humans Neural Networks, Computer Electroencephalography
View on PubMed DOI

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

Sensors (Basel, Switzerland) Sep 29, 2023
A large share of traffic accidents is related to driver fatigue. In recent years, many studies have been organized in order to diagnose and warn drivers. In this research, a new approach was presented in order to detect multi-level driver fatigue. A ...
Fatigue Electroencephalography Accidents, Traffic Humans Automobile Driving Deep Learning
View on PubMed DOI

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

Journal of neural engineering Sep 29, 2023
. Single-trial electroencephalography (EEG) classification is a promising approach to evaluate the cognitive experience associated with haptic feedback. Convolutional neural networks (CNNs), which are among the most widely used deep learning techniqu...
Vibration Algorithms Deep Learning Humans Electroencephalography Neural Networks, Computer
View on PubMed DOI

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

Sensors (Basel, Switzerland) Sep 23, 2023
In recent years, the use of electroencephalography (EEG) has grown as a tool for diagnostic and brain function monitoring, being a simple and non-invasive method compared with other procedures like histological sampling. Typically, in order to extrac...
Brain Neural Networks, Computer Deep Learning Algorithms Electroencephalography Speech Humans Artifacts
View on PubMed DOI
Popular Topics
Recent Journals