Conditional Entropy: A Potential Digital Marker for Stress

Keshmiri, Soheil

doi:10.3390/e23030286

Cited by 14 publications

(9 citation statements)

References 99 publications

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“…These brain rhythms contain relevant information related to mental stress and other psychological disorders. The commonly used spectral EEG features include the power spectral density (PSD), differential asymmetry features, phase synchronization, phase lag index, directed transfer function and entropies [ 29 , 30 , 31 , 32 ]. In addition, the time-frequency features are obtained through the short-time Fourier transform (STFT), or discrete wavelet transform (DWT) [ 33 , 34 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

A Review on Mental Stress Assessment Methods Using EEG Signals

Katmah

Al-Shargie

Tariq

et al. 2021

Sensors

141

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Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contains rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Accordingly, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.

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Section: Introductionmentioning

confidence: 99%

A Review on Mental Stress Assessment Methods Using EEG Signals

Katmah

Al-Shargie

Tariq

et al. 2021

Sensors

141

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“…Previous studies have used EEG to quantify stress states and have found EEG-related markers that can objectively and effectively assess stress ( Peng et al, 2012 ; Minguillon et al, 2016 ; Keshmiri, 2021 ). The present study extended their thoughts to the case of mental fatigue under stress conditions.…”

Section: Discussionmentioning

confidence: 99%

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

Zhang

Wang

Geng

et al. 2021

Front. Hum. Neurosci.

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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.

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“…Numerous studies have shown the association between the EEG pattern and stress level [3], [4], [5], [6], [7]. In [8], a combination of time-domain and frequency-domain analysis using wrapper-based algorithms as features and Boruta as feature selector was proposed to identify stress levels using EEG signals.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Fractal Dimension and Wavelet Transform Methods in Classification of Stress State from EEG Signals

Hamid¹,

Saad²,

Haris³

2022

IJCDS

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Stress is a significant issue in everyday life that affects both physical and mental health. There are different approaches to stress classification. This research examines the implementation of the fractal dimension (FD) method as one of the features for stress state classification using brain signals. Consequently, the comparison between FD and wavelet transform has been conducted using electroencephalogram (EEG) signals recorded during the Stroop Colour Word Test (SCWT). The comparison results show that the FD is better in the classification of the stress state. The highest F1 score has been obtained using FD with quadratic support vector machine (SVM) in average 83.03% for the comparison between baseline session and different stress states. Besides, FD with medium Gaussian SVM has the highest F1 score, on average 83.36%, for comparison between various stress states.

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Conditional Entropy: A Potential Digital Marker for Stress

Cited by 14 publications

References 99 publications

A Review on Mental Stress Assessment Methods Using EEG Signals

A Review on Mental Stress Assessment Methods Using EEG Signals

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

Comparison of Fractal Dimension and Wavelet Transform Methods in Classification of Stress State from EEG Signals

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