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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 …
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
As driving functions become increasingly automated, motorists run the risk of becoming
cognitively removed from the driving process. Psychophysiological measures may provide …
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 …
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
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 …
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
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 …
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
Objective: The driver fatigue detection using multi-channel electroencephalography (EEG)
has been extensively addressed in the literature. However, the employment of a single …
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 …
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
Driver distraction diverting drivers' attention to unrelated tasks and decreasing the ability to
control vehicles, has aroused widespread concern about driving safety. Previous studies …
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
Hybrid brain–computer interfaces (BCI) utilizing the high temporal resolution of
electroencephalography (EEG) and the high spatial resolution of functional near-infrared …
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
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) …
complex structure, whose function can be evaluated with electroencephalogram (EEG) …