EEG-based neural networks approaches for fatigue and drowsiness detection: A survey

A Othmani, AQM Sabri, S Aslan, F Chaieb, H Rameh… - Neurocomputing, 2023 - Elsevier
Drowsiness is a state of fatigue or sleepiness characterized by a strong urge to sleep. It is
correlated with a progressive decline in response time, compromised processing of …

Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN—Part II: Patellofemoral Joint

R Karpiński, P Krakowski, J Jonak, A Machrowska… - Sensors, 2022 - mdpi.com
Cartilage loss due to osteoarthritis (OA) in the patellofemoral joint provokes pain, stiffness,
and restriction of joint motion, which strongly reduces quality of life. Early diagnosis is …

A Vis/NIRS device for evaluating leaf nitrogen content using K-means algorithm and feature extraction methods

M Lu, H Wang, J Xu, Z Wei, Y Li, J Hu, S Tian - Computers and Electronics …, 2024 - Elsevier
Accurate assessing leaf nitrogen content (LNC) is crucial for actual production and fertilizer
management. In this research, a portable device was designed to rapidly and non …

Real-world data-driven machine-learning-based optimal sensor selection approach for equipment fault detection in a thermal power plant

S Khalid, H Hwang, HS Kim - Mathematics, 2021 - mdpi.com
Due to growing electricity demand, developing an efficient fault-detection system in thermal
power plants (TPPs) has become a demanding issue. The most probable reason for failure …

Double handed dynamic Turkish Sign Language recognition using Leap Motion with meta learning approach

Z Katılmış, C Karakuzu - Expert Systems with Applications, 2023 - Elsevier
Sign language is one of the most important communication tools for hearing impaired
people. In this study, the recognition of two-handed dynamic words in Turkish Sign …

Multiclass classification of imagined speech EEG using noise-assisted multivariate empirical mode decomposition and multireceptive field convolutional neural …

H Park, B Lee - Frontiers in human neuroscience, 2023 - frontiersin.org
Introduction In this study, we classified electroencephalography (EEG) data of imagined
speech using signal decomposition and multireceptive convolutional neural network. The …

Miner fatigue detection from electroencephalogram-based relative power spectral topography using convolutional neural network

L Xu, J Li, D Feng - Sensors, 2023 - mdpi.com
Fatigue of miners is caused by intensive workloads, long working hours, and shift-work
schedules. It is one of the major factors increasing the risk of safety problems and work …

An adaptive driver fatigue classification framework using EEG and attention-based hybrid neural network with individual feature subsets

Y Wang, Z Fang, X Sun, X Lin, L Niu, W Ma - Biomedical Signal Processing …, 2023 - Elsevier
Driver fatigue is a major cause of traffic accidents, and electroencephalography (EEG)
based driver fatigue classification is widely regarded as a future direction. In practical …

Optimal feature‐algorithm combination research for EEG fatigue driving detection based on functional brain network

Y Zhou, CQ Zeng, ZD Mu - IET Biometrics, 2023 - Wiley Online Library
With the increasing number of motor vehicles globally, the casualties and property losses
caused by traffic accidents are substantial worldwide. Traffic accidents caused by fatigue …

EEG-FCV: an EEG-based functional connectivity visualization framework for cognitive state evaluation

H Zeng, Y Jin, Q Wu, D Pan, F Xu, Y Zhao, H Hu… - Frontiers in …, 2022 - frontiersin.org
Electroencephalogram (EEG)-based tools for brain functional connectivity (FC) analysis and
visualization play an important role in evaluating brain cognitive function. However, existing …