Volume 7, Issue 2 (Iranian Journal of Ergonomics 2019)                   Iran J Ergon 2019, 7(2): 45-53 | Back to browse issues page

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Fouladi Dehaghi B, Mohammadi A, Nematpour L. Mental Fatigue Assessment using recording Brain Signals: Electroencephalography. Iran J Ergon. 2019; 7 (2) :45-53
URL: http://journal.iehfs.ir/article-1-625-en.html
MSc of Occupational Health, Department of Occupational Safety and Health Engineering, Faculty of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran , Lnematpour94@gmail.com
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Fatigue is a gradual, cumulative process that is thought to be associated with reluctance to make any effort, decrease in efficiency and alertness, and ultimately mental impairment, which can be referred to as mental fatigue (1). The factors that cause fatigue are long hours of work, excessive heat or cold, lack or excess of brightness, irregular sleep, physiological and neurological disorders, uniform activity, social and family problems, and night shift (2).
Electroencephalography (EEG) is a method by which the electrical potentials of the neuronal activity of brain cells called electroencephalograms can be recorded and observed. In this way, different electrical patterns can be recorded in the cortex of the human brain. Patterns of brain activity recording include waves: beta (12–30 Hz), alpha (8–12 Hz), and theta (4–8 Hz) (3). Self-reporting is another way in which one reports fatigue. The visual analog scale is used to implement this method (4).
The purpose of this study was to evaluate mental fatigue and its effect on the accuracy and performance of individuals in mental activity conditions and to relate subjective and objective criteria for measuring mental fatigue in their early stages using EEG and visual analogue scale.


Materials and Methods

In the present descriptive-experimental study, 20 students with mean age (SD) of 24.40 (3.73) participated. The study was conducted at 8:30 am for 75 minutes in a temperature and sound controlled laboratory environment. Participants were asked to avoid caffeine or tea for 4 hours and cigarettes for up to 24 hours before the study (5).
Participants were recorded in five 15-minute steps in the first phase without any activity and their brain activity was recorded by EEG. Then, in the second to fifth stages, in order to evaluate the accuracy and performance, they studied the same literary text with the number of misspellings specified in each step and determined the number of misspellings in the text. At baseline and after every 15 minutes, visual analogue scale was completed by individuals (6).
The NeXus-4 portable device (Mind Media B.V. Company, Netherlands) with a sampling frequency of 1024 Hz and 24 bit using active EEG signal electrodes was used. BioTrace software was used to remove artifacts, perturbations and signal processing (7). International standard 10.20 was used to place the electrodes. Input A was used to measure electroencephalography and the left ear was used for reference electrode (7). SPSS 18 (SPSS Inc., Chicago, IL., USA) was used for data analysis.



The mean-standard deviation of the visual analogue scale at the beginning and end15 minutes of the study are 2.50 /0 0.95 and 6.75 /1 1.11, respectively, as shown in Figure 1. Paired t-test showed that there was a significant difference between the amount of self-evaluation in the first 15 minutes of the study (P<0.05).

Figure 1- Reported level of mental fatigue (visual analogue scale) in 5 steps (1: 15 min, 2: 30 min, 3: 45 min, 4: 60 min, 5: 75 min)

The mean and standard deviation of participants' performance and accuracy decreased with each step (Table 1). There was a significant difference between staff performance in the first and last 15 minutes (P<0.05).

Table 1- Students' accuracy and performance in 5 steps: (1: 0 to 15 minutes, 2: 15 to 30 minutes, 3: 30 to 45 minutes, 4: 45 to 60 minutes, 5: 60 to 75 minutes)

The recorded alpha, beta, and theta waves indicated a declining trend in the first 15 to 30 minutes. They were also almost constant during the test from 15 to 60 minutes. While the alpha wave was decreasing at 60 to 75 minutes, generally a downtrend was observed throughout the diagrams (Figure 2).

Figure 2 - Power changes of brain waves

The power of the alpha, beta, and theta waves in the first and last 15 minutes of the study are presented in Table 2. The mean alpha and beta wave power in the first and the last 15 minutes were significantly different (P=0.050), but no significant difference was observed in the mean theta wave power.

Table 2 - Power of different waves in the first and last 15 minutes

Comparison of students' subjective and objective fatigue by EEG and visual analogue scale using the Pearson test shows that there was no correlation between registered subjective fatigue and reported subjective fatigue in elementary students (Table 3). There was no relationship between EEG and visual analogue scale until the last phase of the experiment (P<0.05). The correlation coefficient obtained is negative at all stages indicating the opposite effect of these two variables on each other.

Table 3. Comparison of subjective and objective fatigue in students



The results of this study showed that the decrease in the levels of alpha, beta and theta waves during the test showed an increase in mental fatigue. There was also a significant increase in fatigue levels at the first and last 15 minutes of the study. Various studies have shown that with increasing levels of mental fatigue, the relative power of theta, alpha and beta rhythms decreases (8, 9). Mitchell et al. (2019) in a study compared the effects of cognitive task on EEG indices of mental fatigue and visual analogue scale. Results showed that participants reported their mental fatigue by visual analogue scale. Also, the alpha, beta, delta, and theta waves variables increased during the experiment, indicating mental fatigue in individuals (10).
Fluctuations in alpha activity were observed in the present study. However, although alpha activity declined overall, it remained steady at 30-60 minutes and increased at 60-75 minutes. At 15 to 30 minutes, a downward slope in beta and theta activity was observed with increasing fatigue, but at 30 to 75 minutes the trend remained constant. Studies have shown that some channels have a direct relationship with increased mental exhaustion and others have a photographic relationship. This means that brain activity in some areas of the head increases with increasing mental fatigue and decreases in some areas, but given the role of most EEG recording channels it can be said that all areas of the head are effective in mental fatigue (11,12).
The results of EEG comparison and visual analogue scale showed no significant correlation between participants in all stages of the study. During the test stages, students reported their mental fatigue as a visual analog scale, but the objective fatigue recorded by the EEG did not show significant changes in the various stages of the test. And, as stated, no statistical correlation was found between them. In the Charbonnier study, mental fatigue in control operators is divided into two groups; the group with mental exhaustion with an index more than 0.4 and the group with an index less than 0.4. Finally, the correlation between KSS and subjects with EEG index above 0.4 was higher. But in individuals less than 0.4 this relationship has not been seen (13).



The results of this study showed that long-term cognitive function increases mental fatigue and decreases performance in individuals. It has also been found that EEG can be objectively effective as a mental fatigue measurement tool and there is a positive relationship between mental fatigue and decreased accuracy, concentration and alertness which can decrease the quality of their performance. The results of this study suggest that brain waves can be good indicators for predicting early mental fatigue.



The authors thank all those who helped them writing this article.


Conflicts of Interest

The authors declared that there are no conflicts of interest.


Type of Study: Research | Subject: Special
Received: 2019/03/30 | Accepted: 2019/06/30 | ePublished: 2019/11/11

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