Exploring Neuro-Physiological Correlates of Drivers' Mental Fatigue Caused by Sleep Deprivation Using Simultaneous EEG, ECG, and fNIRS Data

Most of the people who do not take required sleep are prone to sleep-deprived mental fatigue. This mental fatigue due to sleep deprivation is very harmful to persons involved in critical jobs like Pilots, Surgeons, Air traffic controllers and others. The present research paper proposes an intelligent method based on re-enforced learning, followed by classification supported by the adaptive threshold. Moreover, the method proposed by us is non-intrusive, in which the subject is unaware of being monitored during the test; it helps prevent biased results. The novelty lies in the use of the Inter-frame interval of an open and close eye for feature extraction that leads to the detection of “Alertness” or “Fatigue” based on the adaptive threshold. The proposed self-learning framework is real-time in nature and has a detection accuracy of 97.5 %. Since the method is self-learning, as the size of the data set increases, its accuracy and sensitivity are likely to increase further.

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Postural Stability Change Under Sleep Deprivation and Mental Fatigue Status

Aerospace Medicine and Human Performance ◽

10.3357/amhp.5755.2021 ◽

2021 ◽

Vol 92 (8) ◽

pp. 627-632

Author(s):

Shan Cheng ◽

Jinghua Yang ◽

Miao Su ◽

Jicheng Sun ◽

Kaiwen Xiong ◽

...

Keyword(s):

Heart Rate ◽

Sleep Deprivation ◽

Postural Stability ◽

Young Male ◽

Depression Score ◽

Mental Fatigue ◽

Human Errors ◽

Stability Change ◽

Independent Variable ◽

S Curve

AbstractBACKGROUND: Based on posturography parameters during sleep deprivation (SD), a mental fatigue index (MFI) was constructed for healthy male cadets.METHODS: There were 37 young male subjects who volunteered for two successive days of SD. Their posturography balance, profile of mood status (POMS), and heart rate variability (HRV) were measured at four different times (10:00 and 22:00 of day 1, 10:00 and 22:00 of day 2). According to the methods used in our previous research, similar MFIs based on posturography parameters were computed. Then, correlations of MFIs with POMS scores and HRV values were evaluated by linear and nonlinear methods including quadratic, S-curve, growth, and exponential analyses.RESULTS: MFI continued to increase during SD and MFI as the independent variable had quadratic relationships with fluster (R2 0.057), depression (R2 0.067), and anger (R2 0.05) scores of POMS. A linear correlation was found between MFI and the depression score (R2 0.045) and MFI correlated linearly (R2 0.029) and nonlinearly (R2 0.03) with heart rate. Similarly, MFI reflected changes in the time and frequency domain parameters of HRV, with linear (R2range: 0.0290.082) or nonlinear (R2range: 0.0300.082) relationships.DISCUSSION: The increase of MFI was linked with amplification of personal negative moods and an imbalance of autonomic nervous system activity. The findings suggest that MFI might be a potential indicator of mental fatigue and provide a method to prevent driving fatigue and human errors.Cheng S, Yang J, Su M, Sun J, Xiong K, Ma J, Hu W. Postural stability change under sleep deprivation and mental fatigue status. Aerosp Med Hum Perform. 2021; 92(8):627632.

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An Adaptive EEG Feature Extraction Method Based on Stacked Denoising Autoencoder for Mental Fatigue Connectivity

Neural Plasticity ◽

10.1155/2021/3965385 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Zhongliang Yu ◽

Lili Li ◽

Wenwei Zhang ◽

Hangyuan Lv ◽

Yun Liu ◽

...

Keyword(s):

Feature Extraction ◽

Sleep Deprivation ◽

Signal To Noise Ratio ◽

Principal Component ◽

Mental Fatigue ◽

Connectivity Analysis ◽

Signal To Noise ◽

Denoising Autoencoder ◽

Feature Extraction Method ◽

Noise Ratio

Mental fatigue is a common psychobiological state elected by prolonged cognitive activities. Although, the performance and the disadvantage of the mental fatigue have been well known, its connectivity among the multiareas of the brain has not been thoroughly studied yet. This is important for the clarification of the mental fatigue mechanism. However, the common method of connectivity analysis based on EEG cannot get rid of the interference from strong noise. In this paper, an adaptive feature extraction model based on stacked denoising autoencoder has been proposed. The signal to noise ratio of the extracted feature has been analyzed. Compared with principal component analysis, the proposed method can significantly improve the signal to noise ratio and suppress the noise interference. The proposed method has been applied on the analysis of mental fatigue connectivity. The causal connectivity among the frontal, motor, parietal, and visual areas under the awake, fatigue, and sleep deprivation conditions has been analyzed, and different patterns of connectivity between conditions have been revealed. The connectivity direction under awake condition and sleep deprivation condition is opposite. Moreover, there is a complex and bidirectional connectivity relationship, from the anterior areas to the posterior areas and from the posterior areas to the anterior areas, under fatigue condition. These results imply that there are different brain patterns on the three conditions. This study provides an effective method for EEG analysis. It may be favorable to disclose the underlying mechanism of mental fatigue by connectivity analysis.

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Caffeine and Placebo Expectation

Journal of Psychophysiology ◽

10.1027/0269-8803.21.2.91 ◽

2007 ◽

Vol 21 (2) ◽

pp. 91-99 ◽

Cited By ~ 8

Author(s):

Yunfeng Sun ◽

Yinling Zhang ◽

Ning He ◽

Xufeng Liu ◽

Danmin Miao

Keyword(s):

Blood Pressure ◽

Heart Rate ◽

Sleep Deprivation ◽

Cognitive Performance ◽

Cognitive Functions ◽

Cardiovascular Regulation ◽

Double Blind ◽

Positive Effects ◽

Pressure Tests ◽

Healthy Males

Abstract. Caffeine placebo expectation seems to improve vigilance and cognitive performance. This study investigated the effect of caffeine and placebo expectation on vigilance and cognitive performance during 28 h sleep deprivation. Ten healthy males volunteered to take part in the double-blind, cross-over study, which required participants to complete five treatment periods of 28 h separated by 1-week wash-out intervals. The treatments were no substance (Control); caffeine 200 mg at 00:00 (C200); placebo 200 mg at 00:00 (P200); twice caffeine 200 mg at 00:00 and 04:00 (C200-C200); caffeine 200 mg at 00:00 and placebo 200 mg at 04:00 (C200-P200). Participants were told that all capsules were caffeine and given information about the effects of caffeine to increase expectation. Vigilance was assessed by a three-letter cancellation test, cognitive functions by the continuous addition test and Stroop test, and cardiovascular regulation by heart rate and blood pressure. Tests were performed bihourly from 00:00 to 10:00 of the second day. Results indicated that C200-P200 and C200-C200 were more alert (p < .05) than Control and P200. Their cognitive functions were higher (p < .05) than Control and P200. Also, C200-P200 scored higher than C200 in the letter cancellation task (p < .05). No test showed any significant differences between C200-P200 and C200-C200. The results demonstrated that the combination of caffeine 200 mg and placebo 200 mg expectation exerted prolonged positive effects on vigilance and cognitive performance.

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