Constructing Multi-scale Entropy Based on the Empirical Mode Decomposition(EMD) and its Application in Recognizing Driving Fatigue

Zou, Shuli; Qiu, Taorong; Huang, Peifan; Bai, Xiaoming; Liu, Chao

doi:10.1016/j.jneumeth.2020.108691

Cited by 34 publications

(18 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our reason for monitoring the EEG signal in the PFC region was two-fold. First, the EEG signal characteristics in the PFC region are different between awake and fatigued states, and these characteristics are suitable for assessing changes in fatigue state ( Liu J.P. et al, 2010 ; Li et al, 2012 , 2016 ; Zou et al, 2020 ). Second, in practice, the advantage of measuring the non-hair-bearing scalp area in PFC is that the experiment is easy to prepare and is less invasive.…”

Section: Methodsmentioning

confidence: 99%

Objective Assessments of Mental Fatigue During a Continuous Long-Term Stress Condition

Zhang

Wang

Geng

et al. 2021

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Prolonged periods of cognitive workload will cause mental fatigue, but objective, quantitative, and sensitive measurements that reflect long-term, stress-induced mental fatigue have yet to be elucidated. This study aims to apply a potential marker of Rényi entropy to investigate the mental fatigue changes in a long-term, high-level stress condition and compare three different instruments for assessment of mental fatigue: EEG, the oddball task, and self-scoring. We recruited nine individuals who participated in a 5-day intellectually challenging competition. The participants were assessed for mental fatigue each day of the competition using prefrontal cortex electroencephalogram (EEG). Reaction time in an oddball task and self-rated scoring were used comparatively to evaluate the performance of the EEG. Repeated measures ANOVA was utilized to analyze the differences among score, reaction time, and wavelet Rényi entropy. The results demonstrated that both wavelet Rényi entropy extracted from EEG and self-rated scoring revealed significant increases in mental fatigue during the 5 days of competition (P < 0.001). The reaction time of the oddball task did not show significant changes during the five-day competition (P = 0.066). Moreover, the wavelet Rényi entropy analysis of EEG showed greater sensitivity than the self-rated scoring and reaction time of the oddball task for measuring mental fatigue changes. In conclusion, this study shows that mental fatigue accumulates during long-term, high-level stress situations. The study also indicates that EEG wavelet Rényi entropy is an efficient metric to reflect the change of mental fatigue under a long-term stress condition and that EEG is a better method to assess long-term mental fatigue.

show abstract

Section: Methodsmentioning

confidence: 99%

Objective Assessments of Mental Fatigue During a Continuous Long-Term Stress Condition

Zhang

Wang

Geng

et al. 2021

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…The authors used only the O2 channel of the EEG signal and performed a continuous wavelet transform to obtain the PSD. Zou et al [ 176 ] used multiscale PE, multiscale SampEn, and multiscale FuzzyEn. Their ground truth labels were based on Li’s subjective fatigue scale and the accuracy achieved was 88.74%.…”

Section: Driver Drowsiness Detection Systemsmentioning

confidence: 99%

A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems

Stančin¹,

Cifrek²,

Jović³

2021

Sensors

131

View full text Add to dashboard Cite

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 system. In this context, electroencephalogram (EEG) signals are the most important source of data to achieve successful detection. In this paper, we first review EEG signal features used in the literature for a variety of tasks, then we focus on reviewing the applications of EEG features and deep learning approaches in driver drowsiness detection, and finally we discuss the open challenges and opportunities in improving driver drowsiness detection based on EEG. We show that the number of studies on driver drowsiness detection systems has increased in recent years and that future systems need to consider the wide variety of EEG signal features and deep learning approaches to increase the accuracy of detection.

show abstract

“…Empirical mode decomposition (EMD) is one of the most widely used signal decomposition techniques. For example, Zou et al, [ 45 ] utilized EMD combined with multiscale entropy to recognize driving fatigue. This is mainly caused by the fact that the EMD method has good adaptability, internality, and objectivity.…”

Section: Brief Literature Reviewmentioning

confidence: 99%

“…This is mainly caused by the fact that the EMD method has good adaptability, internality, and objectivity. [ 46–49 ] It can adaptively extract the local mean curve of nonstable data, and decompose the complex superimposed signal into limited and physically intrinsic mode functions (IMFs). Then, a significant instantaneous frequency and Hilbert time spectrum can be obtained.…”

Section: Brief Literature Reviewmentioning

confidence: 99%

Wind Speed Prediction in the Hexi Corridor Region of China Using an Enhanced Multiscale Nonlinear Ensemble Model

Wang

Cheng

2021

Energy Tech

View full text Add to dashboard Cite

Accurate wind speed prediction has become more and more popular. Herein, an enhanced multi‐scale nonlinear ensemble model is proposed to predict short‐term wind speed. First, advanced data preprocessing technology is used to extract these feature sequences. Second, each feature sequence is predicted by bidirectional long short‐term memory. In addition, the gated recurrent unit (GRU) realizes nonlinear ensemble and integrates the prediction information. Finally, GRU with white noise test is utilized to correct the residual to retain the possible effective information in the residual. The performance of the model is evaluated using the daily and hourly wind speeds of two wind farms in the Hexi Corridor region. The experimental results show that the proposed model has the best prediction accuracy among all relevant models. Taking Zhangye wind farm as an example, in the daily wind speed dataset, mean absolute percentage error, square sum error, theil inequality coefficient and the index of agreement of the model are 0.6416, 5.4234, 0.0151, and 0.9979, respectively. In the hourly dataset, they are 0.1037, 0.0042, 0.0003, and 1.0000, respectively. In the wind speed data of the Guazhou wind farm, the hybrid model proposed in this research also performs best.

show abstract

Constructing Multi-scale Entropy Based on the Empirical Mode Decomposition(EMD) and its Application in Recognizing Driving Fatigue

Cited by 34 publications

References 32 publications

Objective Assessments of Mental Fatigue During a Continuous Long-Term Stress Condition

Objective Assessments of Mental Fatigue During a Continuous Long-Term Stress Condition

A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems

Wind Speed Prediction in the Hexi Corridor Region of China Using an Enhanced Multiscale Nonlinear Ensemble Model

Contact Info

Product

Resources

About