Mental Fatigue Estimation Using EEG in a Vigilance Task and Resting States

Tian, Sen; Wang, Yijun; Chen, Hongda

doi:10.1109/embc.2018.8512666

Cited by 17 publications

(8 citation statements)

References 12 publications

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“…With the mental fatigue increasing, the power spectrum density of alpha rhythm increases when eyes open and decreases when eyes closed [17]. These changes in EEG can be used to detect mental fatigue [10,15], which is especially important and meaningful for driving fatigue estimation [7,11]. From the above, we can conclude that EEG has become the most effective technical means for exploring the neuromechanism and detection of mental fatigue [18,19].…”

Section: Introductionmentioning

confidence: 85%

“…Historically, mental fatigue has been most prevalently studied with the neuroimaging technique of EEG on account of its high temporal resolution, low costs, and easy operation [12][13][14]. It has been widely proved that mental fatigue can lead to distinct changes in EEG [15]. Strijkstra has found that EEG during resting awake periods with eyes closed shows strong negative correlations of alpha power and positive correlations of theta power with subjective sleepiness [16].…”

Section: Introductionmentioning

confidence: 99%

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The impact of mental fatigue on brain activity: A comparative study between resting state and task state using EEG

Luo

Jiao

et al. 2019

Preprint

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Background: Mental fatigue is usually caused by long-term cognitive activities, mainly manifested as drowsiness, difficulty in concentrating, decreased alertness, disordered thinking, slow reaction, lethargy, reduced work efficiency, error-prone and so on. Mental fatigue has become a widespread sub-health condition, and has a serious impact on the cognitive function of the brain. However, seldom researches explore the differences of mental fatigue on electrophysiological activity between resting state and task state. In the present study, 20 healthy male individuals were recruited to do a consecutive mental arithmetic task to induce mental fatigue, and scalp electroencephalogram (EEG) data were collected before and after the task. The power and relative power of five EEG rhythms both in resting state and task state were analyzed statistically. Results: The results of brain topographies and statistical analysis indicated that mental arithmetic task can successfully induce mental fatigue in the enrolled subjects. The relative power index was more sensitive than the power index in response to mental fatigue, and the relative power for assessing mental fatigue was better in resting state than in task state. Furthermore, we found that it is of great physiological significance to divide alpha frequency band into alpha1 band and alpha2 band in fatigue related studies, and at the same time improve the statistical differences of sub-bands. Conclusions: Our current results suggested that the brain activity in mental fatigue state has great differences between resting state and task state, and it is imperative to select the appropriate state in EEG data acquisition and divide alpha band into alpha1 and alpha2 bands in mental fatigue related researches.

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Section: Introductionmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

The impact of mental fatigue on brain activity: A comparative study between resting state and task state using EEG

Luo

Jiao

et al. 2019

Preprint

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“…Modern life, with its high-stress life and work, has resulted in mental fatigue becoming a common phenomenon, including feelings of exhaustion, depletion, an aversion to continuing tasks, and a decrease in commitment to ongoing tasks [ 1 , 2 ]. Mental fatigue can negatively affect individual-level cognitive abilities, emotional management, and behavioral control [ 3 , 4 ], causing decreased alertness [ 5 ], a narrowed attention span [ 6 , 7 ], diminished short-term memory [ 8 ], and reduced emotion regulation [ 3 ], leading to increased risky decision-making [ 9 ] and decreased work efficiency. Fatigue also leads to accidents in different fields, such as in the medical industry [ 10 ], on the military field [ 11 , 12 ], and in transportation [ 11 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Listening to 15 Hz Binaural Beats Enhances the Connectivity of Functional Brain Networks in the Mental Fatigue State—An EEG Study

Wang

et al. 2022

Brain Sciences

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Introduction: It is clear that mental fatigue can have many negative impacts on individuals, such as impairing cognitive function or affecting performance. The aim of this study was to investigate the role of sound interventions in combating mental fatigue. Method: The subjects were assessed on various scales, a psychomotor vigilance task (PVT) task, and a 3 min resting-state electroencephalogram (EEG), followed by a 20 min mental fatigue–inducing task (Time Load Dual Back ,TloadDback), during which subjects in different condition groups listened to either 15 Hz binaural beats, 40 Hz binaural beats, relaxing music, or a 240 Hz pure tone. After the mental fatigue–inducing task, subjects were again assessed on various scales, a PVT task, and a 3 min resting-state EEG. Results: After the fatigue-inducing task, there was no significant difference between the four groups on the scales or the PVT task performance. In TloadDback, the accuracy rate of the 40 Hz binaural beats group and the relaxing music group decreased in the middle stage of the task, while the 15 Hz binaural beats group and the 240 Hz pure tone group remained unchanged in all stages of the task. The EEG results showed that after fatigue inducement, the average path length of the 15 Hz binaural beats group decreased, and local efficiency showed an increasing tendency, indicating enhanced brain network connectivity. Meanwhile, the 240 Hz pure tone group showed enhanced functional connectivity, suggesting a state of mental fatigue in the group. Conclusions: The results of this study show that listening to 15 Hz binaural beats is a proven intervention for mental fatigue that can contribute to maintaining working memory function, enhancing brain topological structure, and alleviating the decline in brain function that occurs in a mentally fatigued state. As such, these results are of great scientific and practical value.

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“…In response to these critical issues, several methods (Cook et al, 2007;Blankertz et al, 2009;Fazli et al, 2009;Wascher et al, 2014;Tian et al, 2018;Kaji et al, 2019) have been proposed to estimate and predict the mental fatigue based on electroencephalography (EEG) and reaction time (RT) (Fig. 1(a)).…”

Section: Introductionmentioning

confidence: 99%

Stochastic Multichannel Ranking with Brain Dynamics Preferences

et al. 2020

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A driver's cognitive state of mental fatigue significantly affects his or her driving performance and more important, public safety. Previous studies have leveraged reaction time (RT) as the metric for mental fatigue and aim at estimating the exact value of RT using electroencephalogram (EEG) signals within a regression model. However, due to the easily corrupted and also nonsmooth properties of RTs during data collection, methods focusing on predicting the exact value of a noisy measurement, RT generally suffer from poor generalization performance. Considering that human RT is the reflection of brain dynamics preference (BDP) rather than a single regression output of EEG signals, we propose a novel channel-reliability-aware ranking (CArank) model for the multichannel ranking problem. CArank learns from BDPs using EEG data robustly and aims at preserving the ordering corresponding to RTs. In particular, we introduce a transition matrix to characterize the reliability of each channel used in the EEG data, which helps in learning with BDPs only from informative EEG channels. To handle large-scale EEG signals, we propose a stochastic-generalized expectation maximum (SGEM) algorithm to update CArank in an online fashion. Comprehensive empirical analysis on EEG signals from 40 participants shows that our CArank achieves substantial improvements in reliability while simultaneously detecting noisy or less informative EEG channels.

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Mental Fatigue Estimation Using EEG in a Vigilance Task and Resting States

Cited by 17 publications

References 12 publications

The impact of mental fatigue on brain activity: A comparative study between resting state and task state using EEG

The impact of mental fatigue on brain activity: A comparative study between resting state and task state using EEG

Listening to 15 Hz Binaural Beats Enhances the Connectivity of Functional Brain Networks in the Mental Fatigue State—An EEG Study

Stochastic Multichannel Ranking with Brain Dynamics Preferences

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