[HTML][HTML] Wearable electroencephalography and multi-modal mental state classification: A systematic literature review
Background: Wearable multi-modal time-series classification applications outperform their
best uni-modal counterparts and hold great promise. A modality that directly measures …
best uni-modal counterparts and hold great promise. A modality that directly measures …
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
challenging to design a calibration-free system, since EEG signals vary significantly among …
challenging to design a calibration-free system, since EEG signals vary significantly among …
[PDF][PDF] Recent Advances in Fatigue Detection Algorithm Based on EEG.
F Wang, Y Wan, M Li, H Huang, L Li… - … Automation & Soft …, 2023 - cdn.techscience.cn
Fatigue is a state commonly caused by overworked, which seriously affects daily work and
life. How to detect mental fatigue has always been a hot spot for researchers to explore …
life. How to detect mental fatigue has always been a hot spot for researchers to explore …
A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG
Driver drowsiness is one of the main factors leading to road fatalities and hazards in the
transportation industry. Electroencephalography (EEG) has been considered as one of the …
transportation industry. Electroencephalography (EEG) has been considered as one of the …
[HTML][HTML] A decomposition-based hybrid ensemble CNN framework for driver fatigue recognition
Electroencephalogram (EEG) has become increasingly popular in driver fatigue monitoring
systems. Several decomposition methods have been attempted to analyze the EEG signals …
systems. Several decomposition methods have been attempted to analyze the EEG signals …
Foundations and applicability of transfer learning for structural health monitoring of bridges
The number of bridges worldwide is extensive, making it financially and technically
challenging for the authorities to install a structural health monitoring (SHM) system and …
challenging for the authorities to install a structural health monitoring (SHM) system and …
Developing a deep neural network for driver fatigue detection using EEG signals based on compressed sensing
In recent years, driver fatigue has become one of the main causes of road accidents. As a
result, fatigue detection systems have been developed to warn drivers, and, among the …
result, fatigue detection systems have been developed to warn drivers, and, among the …
Recognising drivers' mental fatigue based on EEG multi-dimensional feature selection and fusion
Y Zhang, H Guo, Y Zhou, C Xu, Y Liao - Biomedical Signal Processing and …, 2023 - Elsevier
Detecting the mental state of a driver using electroencephalography (EEG) signals can
reduce the probability of traffic accidents. However, EEG signals are unstable and nonlinear …
reduce the probability of traffic accidents. However, EEG signals are unstable and nonlinear …
Cross-subject zero calibration driver's drowsiness detection: Exploring spatiotemporal image encoding of EEG signals for convolutional neural network classification
This paper explores two methodologies for drowsiness detection using EEG signals in a
sustained-attention driving task considering pre-event time windows, and focusing on cross …
sustained-attention driving task considering pre-event time windows, and focusing on cross …
Subject matching for cross-subject EEG-based recognition of driver states related to situation awareness
Situation awareness (SA) has received much attention in recent years because of its
importance for operators of dynamic systems. Electroencephalography (EEG) can be used …
importance for operators of dynamic systems. Electroencephalography (EEG) can be used …