Concurrent fNIRS and EEG for brain function investigation: a systematic, methodology-focused review
Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as
state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis …
state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis …
Deep learning in physiological signal data: A survey
Deep Learning (DL), a successful promising approach for discriminative and generative
tasks, has recently proved its high potential in 2D medical imaging analysis; however …
tasks, has recently proved its high potential in 2D medical imaging analysis; however …
A driving fatigue feature detection method based on multifractal theory
F Wang, H Wang, X Zhou, R Fu - IEEE Sensors Journal, 2022 - ieeexplore.ieee.org
Driving fatigue seriously threatens traffic safety. In our work, the multifractal detrended
fluctuation analysis (MF-DFA) method is proposed to detect driver fatigue caused by driving …
fluctuation analysis (MF-DFA) method is proposed to detect driver fatigue caused by driving …
IoT‐based smart alert system for drowsy driver detection
In current years, drowsy driver detection is the most necessary procedure to prevent any
road accidents, probably worldwide. The aim of this study was to construct a smart alert …
road accidents, probably worldwide. The aim of this study was to construct a smart alert …
Convolutional neural network for drowsiness detection using EEG signals
Drowsiness detection (DD) has become a relevant area of active research in biomedical
signal processing. Recently, various deep learning (DL) researches based on the EEG …
signal processing. Recently, various deep learning (DL) researches based on the EEG …
Deep learning in EEG: Advance of the last ten-year critical period
Deep learning has achieved excellent performance in a wide range of domains, especially
in speech recognition and computer vision. Relatively less work has been done for …
in speech recognition and computer vision. Relatively less work has been done for …
Linking attention-based multiscale CNN with dynamical GCN for driving fatigue detection
Electroencephalography (EEG) signals have been proven to be one of the most predictive
and reliable indicators for estimating driving fatigue state. However, how to make full use of …
and reliable indicators for estimating driving fatigue state. However, how to make full use of …
A review of driver fatigue detection and its advances on the use of RGB-D camera and deep learning
Driver fatigue is an essential reason for traffic accidents, which poses a severe threat to
people's lives and property. In this review, we summarize the latest research findings and …
people's lives and property. In this review, we summarize the latest research findings and …
Continuous EEG decoding of pilots' mental states using multiple feature block-based convolutional neural network
Non-invasive brain-computer interface (BCI) has been developed for recognizing and
classifying human mental states with high performances. Specifically, classifying pilots' …
classifying human mental states with high performances. Specifically, classifying pilots' …
EEG-based neural networks approaches for fatigue and drowsiness detection: A survey
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 …
correlated with a progressive decline in response time, compromised processing of …