A review of EEG signal features and their application in driver drowsiness detection systems
Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that
is often approached using neurophysiological signals as the basis for building a reliable …
is often approached using neurophysiological signals as the basis for building a reliable …
Impact of eeg parameters detecting dementia diseases: A systematic review
LM Sánchez-Reyes, J Rodríguez-Reséndiz… - IEEE …, 2021 - ieeexplore.ieee.org
Dementia diseases are increasing rapidly, according to the World Health Organization
(WHO), becoming an alarming problem for the health sector. The electroencephalogram …
(WHO), becoming an alarming problem for the health sector. The electroencephalogram …
Driver fatigue detection based on convolutional neural networks using em‐CNN
Z Zhao, N Zhou, L Zhang, H Yan, Y Xu… - Computational …, 2020 - Wiley Online Library
With a focus on fatigue driving detection research, a fully automated driver fatigue status
detection algorithm using driving images is proposed. In the proposed algorithm, the …
detection algorithm using driving images is proposed. In the proposed algorithm, the …
Artificial Intelligence, Machine Learning and Reasoning in Health Informatics—Case Studies
Abstract To apply Artificial Intelligence (AI), Machine Learning (ML) and Machine Reasoning
(MR) in health informatics are often challenging as they comprise with multivariate …
(MR) in health informatics are often challenging as they comprise with multivariate …
Driver sleepiness detection from EEG and EOG signals using GAN and LSTM networks
Y Jiao, Y Deng, Y Luo, BL Lu - Neurocomputing, 2020 - Elsevier
In recent years, sleepiness during driving has become a main cause for traffic accidents.
However, the fact is that we know very little yet about the electrophysiological marker for …
However, the fact is that we know very little yet about the electrophysiological marker for …
Physiological signal-based drowsiness detection using machine learning: Singular and hybrid signal approaches
Introduction: Drowsiness is one of the main contributors to road-related crashes and
fatalities worldwide. To address this pressing global issue, researchers are continuing to …
fatalities worldwide. To address this pressing global issue, researchers are continuing to …
Sub-band target alignment common spatial pattern in brain-computer interface
Background and objective In the brain computer interface (BCI) field, using sub-band
common spatial pattern (SBCSP) and filter bank common spatial pattern (FBCSP) can …
common spatial pattern (SBCSP) and filter bank common spatial pattern (FBCSP) can …
EEG based emotion detection using fourth order spectral moment and deep learning
VM Joshi, RB Ghongade - Biomedical Signal Processing and Control, 2021 - Elsevier
This paper proposes emotion detection using Electroencephalography (EEG) signal based
on Linear Formulation of Differential Entropy (LF-D f E) feature extractor and BiLSTM …
on Linear Formulation of Differential Entropy (LF-D f E) feature extractor and BiLSTM …
Self-paced dynamic infinite mixture model for fatigue evaluation of pilots' brains
Current brain cognitive models are insufficient in handling outliers and dynamics of
electroencephalogram (EEG) signals. This article presents a novel self-paced dynamic …
electroencephalogram (EEG) signals. This article presents a novel self-paced dynamic …
Automatic driver sleepiness detection using EEG, EOG and contextual information
The many vehicle crashes that are caused by driver sleepiness each year advocates the
development of automated driver sleepiness detection (ADSD) systems. This study …
development of automated driver sleepiness detection (ADSD) systems. This study …