A comparative analysis of signal processing and classification methods for different applications based on EEG signals

A Khosla, P Khandnor, T Chand - Biocybernetics and Biomedical …, 2020‏ - Elsevier
Electroencephalogram (EEG) measures the neuronal activities in the form of electric
currents that are generated due to the synchronized activity by a group of specialized …

A review of psychophysiological measures to assess cognitive states in real-world driving

M Lohani, BR Payne, DL Strayer - Frontiers in human neuroscience, 2019‏ - frontiersin.org
As driving functions become increasingly automated, motorists run the risk of becoming
cognitively removed from the driving process. Psychophysiological measures may provide …

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 …

A wearable EEG instrument for real-time frontal asymmetry monitoring in worker stress analysis

P Arpaia, N Moccaldi, R Prevete… - IEEE Transactions …, 2020‏ - ieeexplore.ieee.org
A highly wearable single-channel instrument, conceived with off-the-shelf components and
dry electrodes, is proposed for detecting human stress in real time by …

[HTML][HTML] EEG-based driving fatigue detection using multilevel feature extraction and iterative hybrid feature selection

T Tuncer, S Dogan, A Subasi - Biomedical Signal Processing and Control, 2021‏ - Elsevier
Brain activities can be evaluated by using Electroencephalogram (EEG) signals. One of the
primary reasons for traffic accidents is driver fatigue, which can be identified by using EEG …

Fusion of EEG and eye blink analysis for detection of driver fatigue

M Shahbakhti, M Beiramvand, E Nasiri… - … on Neural Systems …, 2023‏ - ieeexplore.ieee.org
Objective: The driver fatigue detection using multi-channel electroencephalography (EEG)
has been extensively addressed in the literature. However, the employment of a single …

Driver fatigue detection based on prefrontal EEG using multi-entropy measures and hybrid model

J Min, C **ong, Y Zhang, M Cai - Biomedical Signal Processing and Control, 2021‏ - Elsevier
The development of a real-time monitoring and warning platform for driver fatigue detection
is of great importance to avoid traffic accidents and fatalities. Due to the explosive growth of …

Driver distraction detection using bidirectional long short-term network based on multiscale entropy of EEG

X Zuo, C Zhang, F Cong, J Zhao… - IEEE Transactions on …, 2022‏ - ieeexplore.ieee.org
Driver distraction diverting drivers' attention to unrelated tasks and decreasing the ability to
control vehicles, has aroused widespread concern about driving safety. Previous studies …

Hybrid EEG-fNIRS brain computer interface based on common spatial pattern by using EEG-informed general linear model

Y Gao, B Jia, M Houston… - IEEE Transactions on …, 2023‏ - ieeexplore.ieee.org
Hybrid brain–computer interfaces (BCI) utilizing the high temporal resolution of
electroencephalography (EEG) and the high spatial resolution of functional near-infrared …

A dynamic center and multi threshold point based stable feature extraction network for driver fatigue detection utilizing EEG signals

T Tuncer, S Dogan, F Ertam, A Subasi - Cognitive neurodynamics, 2021‏ - Springer
Driver fatigue is the one of the main reasons of the traffic accidents. The human brain is a
complex structure, whose function can be evaluated with electroencephalogram (EEG) …