In the present paper, the contribution of the largest principal component and the number of principal component needed for accumulative contribution 95% are selected as indices of electroencephalogram (EEG) in mental fatigue state in order to investigate the relationship between these parameters and mental fatigue. The experimental results showed that the contribution of the largest principal component of EEG signals increased in the prefrontal, frontal and central areas, while the number of principal component needed for accumulative contribution decreased by 95% with the increasing mental fatigue level. The parameters of singular system of EEG signals can be regarded as useful features for the estimation of mental fatigue and have larger application value in the study of mental fatigue.
Mental fatigue is an important factor of human health and safety. It is important to achieve dynamic mental fatigue detection by using electroencephalogram (EEG) signals for fatigue prevention and job performance improvement. We in our study induced subjects' mental fatigue with 30 h sleep deprivation (SD) in the experiment. We extracted EEG features, including relative power, power ratio, center of gravity frequency (CGF), and basic relative power ratio. Then we built mental fatigue prediction model by using regression analysis. And we conducted lead optimization for prediction model. Result showed that R2 of prediction model could reach to 0.932. After lead optimization, 4 leads were used to build prediction model, in which R2 could reach to 0.811. It can meet the daily application accuracy of mental fatigue prediction.
Fatigue is an exhaustion state caused by prolonged physical work and mental work, which can reduce working efficiency and even cause industrial accidents. Fatigue is a complex concept involving both physiological and psychological factors. Fatigue can cause a decline of concentration and work performance and induce chronic diseases. Prolonged fatigue may endanger life safety. In most of the scenarios, physical and mental workloads co-lead operator into fatigue state. Thus, it is very important to study the interaction influence and its neural mechanisms between physical and mental fatigues. This paper introduces recent progresses on the interaction effects and discusses some research challenges and future development directions. It is believed that mutual influence between physical fatigue and mental fatigue may occur in the central nervous system. Revealing the basal ganglia function and dopamine release may be important to explore the neural mechanisms between physical fatigue and mental fatigue. Future effort is to optimize fatigue models, to evaluate parameters and to explore the neural mechanisms so as to provide scientific basis and theoretical guidance for complex task designs and fatigue monitoring.
This study is aimed to investigate objective indicators of mental fatigue evaluation to improve the accuracy of mental fatigue evaluation. Mental fatigue was induced by a sustained cognitive task. The brain functional networks in two states (normal state and mental fatigue state) were constructed based on electroencephalogram (EEG) data. This study used complex network theory to calculate and analyze nodal characteristics parameters (degree, betweenness centrality, clustering coefficient and average path length of node), and served them as the classification features of support vector machine (SVM). Parameters of the SVM model were optimized by gird search based on 6-fold cross validation. Then, the subjects were classified. The results show that characteristic parameters of node of brain function networks can be divided into normal state and mental fatigue state, which can be used in the objective evaluation of mental fatigue state.
The electroencephalographic characteristics of mental fatigue, which was induced by long-term working memory task of 2-back, were studied by event-related potential (ERP) technology in order to obtain objective evaluation indicators for mental fatigue. Thirty-two healthy male subjects, 22–28 years old, were divided into two groups evenly, one is un-fatigue group and the other is fatigue group. The fatigue group performed a 2-back task for 100 min continuously, while the un-fatigue group just performed a 2-back task at the first and last 10 min respectively, and rested during the middle 80 min. The subjective levels of fatigue, task performance and electroencephalogram were recorded. The impaired thought and attention states, enhanced sleepy and fatigue feeling were found in the fatigue group, meanwhile their reaction time to 2-back task extended, and the accuracy decreased significantly. These results verified the validity of mental fatigue model induced by 2-back task, and then the ERP characteristic parameters were compared and analyzed between fatigue group and un-fatigue group. The results showed that the fatigue group’s amplitudes of P300 (F = 2.539, P < 0.05) and error-related negativity (ERN) ( F = 10.040, P < 0.05) decreased significantly along with the increase of fatigue comparing with the un-fatigue group, however, there were no significant change in other parameters (all P > 0.05). These results demonstrate that P300 and ERN can be considered as potential evaluation indictors for mental fatigue induced by long-term working memory task, which will provide basis for the future exploring of countermeasure for mental fatigue.
Mental fatigue is a subjective fatigue state caused by long-term brain activity, which is the core of health problems among brainworkers. However, its influence on the process of brain information transmission integration is not clear. In this paper, phase amplitude coupling (PAC) between theta and gamma rhythm was used to study the electroencephalogram (EEG) data recorded before and after mental fatigue, so as to explain the effect of mental fatigue on brain information transmission mechanism. The experiment used a 4-hour professional English reading to induce brain fatigue. EEG signals of 14 male undergraduate volunteers before and after mental fatigue were recorded by Neuroscan EEG system. Phase amplitude coupling value was calculated and analyzed. t test was used to compare the results between two states. The results showed that theta phase of more than 90% of the electrodes in the whole brain area jointly modulated gamma amplitude of the right central area and the right parietal area, and the coupling effect among different brain regions significantly decreased (P < 0.05) when participants had felt mental fatigue. This paper shows that phase amplitude coupling can explain the influence of mental fatigue on information transmission mechanism. It could be an important indicator for mental fatigue detection. On the other hand, the results also provide a new measure to evaluate the effect of neuromodulation in relieving mental fatigue.
Mental fatigue is the subjective state of people after excessive consumption of information resources. Its impact on cognitive activities is mainly manifested as decreased alertness, poor memory and inattention, which is highly related to the performance after impaired working memory. In this paper, the partial directional coherence method was used to calculate the coherence coefficient of scalp electroencephalogram (EEG) of each electrode. The analysis of brain network and its attribute parameters was used to explore the changes of information resource allocation of working memory under mental fatigue. Mental fatigue was quickly induced by the experimental paradigm of adaptive N-back working memory. Twenty-five healthy college students were randomly recruited as subjects, including 14 males and 11 females, aged from 20 to 27 years old, all right-handed. The behavioral data and resting scalp EEG data were collected simultaneously. The results showed that the main information transmission pathway of the brain changed under mental fatigue, mainly in the frontal lobe and parietal lobe. The significant changes in brain network parameters indicated that the information transmission path of the brain decreased and the efficiency of information transmission decreased significantly. In the causal flow of each electrode and the information flow of each brain region, the inflow of information resources in the frontal lobe decreased under mental fatigue. Although the parietal lobe region and occipital lobe region became the main functional connection areas in the fatigue state, the inflow of information resources in these two regions was still reduced as a whole. These results indicated that mental fatigue affected the information resources allocation of working memory, especially in the frontal and parietal regions which were closely related to working memory.