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Investigating EEG Alpha Variations for Mental Fatigue Detection on Car Driving Simulator
Faramarz Gharagozlou, Jebraeil Nasl Saraji, Adel Mazloumi, Ali Nahvi, Ali Motie Nasrabadi, Abbas Rahimi Foroushani, Mohammadreza Ashouri, Mehdi Samavati,
Volume 1, Issue 1 (9-2013)
Abstract

Introduction: Driver fatigue is one of the major causes of accidents in roads. It is suggested that driver fatigue and drowsiness accounted for more than 30% of road accidents. Therefore, it is important to use features for real-time detection of driver mental fatigue to minimize transportation fatalities. The purpose of this study was to explore the EEG alpha power variations in sleep deprived drivers on a car driving simulator.

Materials and Methods: The present descriptive-analytical study was achieved on nineteen healthy male car drivers. After taking informed written consent, the subjects were requested to stay awake 18 hrs before the experiments and refrain from caffeinated drinks or any other stimulant as well as cigarette smoking for 12 hrs prior to the experiments. The drivers sleep patterns were studied through sleep diary for one week before the experiment. The participants performed a simulated driving task in a 110 Km monotonous route at the fixed speed of 90 km/hr. The subjective self-assessment of fatigue was performed in every 10 minute interval during the driving using Karolinska Sleepiness Scale (KSS). At the same time, video recordings from the drivers face and their behaviors were achieved in lateral and front views and rated by two trained observers. Continuous EEG and EOG records were taken with 16 channels during driving. After filtering and artifact removal, power spectrum density and fast Fourier transform (FFT) were used to determine the absolute and relative alpha powers in the initial and final 10 minutes of driving. To analyze the data, descriptive statistics, Pearson and Spearman coefficients and paired-sample T test were employed to describe and compare the variables.

Results: The findings showed a significant increase in KSS scores in the final 10 minutes of driving (p<0.001). Similar results were obtained concerning video rating scores. Meanwhile, there was a significant increase in the absolute alpha power during the final section of driving (p=0.006).

Conclusion: Driver mental fatigue is considered as one of the major implications for road safety. This study suggests that alpha brain wave rhythm can be a good indicator for early prediction of driver fatigue.

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Investigation of Relationship between Noise Annoyance and Neurophysiological Responses of Drivers in Exposure to Tractor Sound
Majid Lashgari, Mohammadreza Arab,
Volume 6, Issue 3 (10-2018)
Abstract
Background & Objectives: Sound as a detrimental factor in working environments can create annoying conditions for people in addition to physical problems. Therefore, in addition to evaluating quantitative parameters such as pressure levels, it is absolutely necessary to study the quality parameters of the sound in the work environment. 
Methods: In this descriptive-analytic research, the sound of 285 MF tractor was recorded. Then, the EEG of five drivers were recorded in the pre-driving state and then when driving with the tractor in four different engine speed. The psychoacoustic annoyance model was used to assess the annoyance of tractor drivers. Then means were compared with Duncan comparison test at 5% probability level and the correlation between psychoacoustic acoustic and alpha and beta bands was determined.
Results: The results of ANOVA showed that different levels of engine speed on psychoacoustic annoyance were significant at 1% probability level. The results also showed a decrease in the amplitude of the alpha band, as well as an increase in the beta band amplitude due to increased engine speed. Regression results showed that there is a high correlation between the two alpha and beta bands and the psychoacoustic annoyance, so that the detection coefficient was 0.966 and 0.998, respectively, for the two bands alpha and beta. 
Conclusion: This study showed that changes in the quality parameters of the sound and consequently the resulting annoyance caused the amplitude changes in both the alpha and beta bands. So, it can be concluded that the psychoacoustic annoyance is a good indicator of brain activity.
 

Mental Fatigue Assessment using recording Brain Signals: Electroencephalography
Behzad Fouladi Dehaghi, Abbas Mohammadi, Leila Nematpour,
Volume 7, Issue 2 (9-2019)
Abstract

Background and Objectives: Mental fatigue is a condition triggered by prolonged cognitive activity. Mental fatigue causes brain over-activity. This is a condition where the brain cells become exhausted, hampering person productivity, and overall cognitive function. The aim of this study was to assess students’ mental fatigue using brain indices.
Methods: The present descriptive - analytic study has been conducted on 20 students of the Faculty of Health mean age (SD) of 24.40 (3.73) years old in Ahwaz University of Medical Sciences (2019). To assess the performance of the participants, they were asked to study a text with spelling errors and correct those errors. This activity was performed in five stages, each lasting 15 min and EEG was recorded at all stages, and at each stage, the visual analog scale was completed by participants. Data analysis was done by SPSS 24.
Results: The results showed that the activity of alpha, beta, and theta signals in the first 15 minutes was 0.89±0.30, 0.70±0.33, and 1.19±0.36, and the last 15 minutes, 0.63±0.34, 0.55±0.26, and 1.03±0.34 respectively. Reducing the activity of the signals indicated there has been an increase in the amount of mental fatigue in individuals. Also, using visual analog scale, the individuals have acknowledged that they have experienced symptoms of mental fatigue. Finally, there was no significant relationship between students’ EEG and visual analog scale.
Conclusion: The results showed that alpha, beta and theta indices could be suitable indicators for evaluating mental fatigue. Also, mental fatigue can be one of the factors that affect the accuracy and performance of individuals, so that it can reduce their attention and efficiency.


 


Correlation Brain Signals and Tractor Sound Signals Based on Fractal Analysis
Majid Lashgari, Mohammadreza Arab, Mohsen Nadjafi, Ali Maleki,
Volume 9, Issue 2 (10-2021)
Abstract
Background & Objectives: Due to the sound caused by various machines and tools in different agriculture sectors, occupational safety and health should be continuously evaluated. Indeed, the harmful effects of sound can be better reduced when the effects of sound on people's health and performance are fully known.
Methods: In this study, a garden tractor was used. Sixteen volunteers were exposed to the sound of the tractor, and their EEG was recorded at four different engine speeds. Then, Higuchi and Katz methods were used to calculate the fractal dimension of sound signals as well as brain signals.
Results: The results showed that by increasing engine speed, the values ​​of the fractal dimension in both Higuchi and Katz methods increased. The results also showed an increase in the fractal dimension of brain signals due to an increase in engine speed. The regression results also showed a high correlation between the two brain signals and the sound. The coefficient of explanation was 0.896 and 0.859 in Higuchi and Katz methods, respectively.
Conclusion: This study showed that people's reactions, when exposed to sound, can be predicted using the fractal dimension. Therefore, it is possible to estimate the characteristics of brain signals without recording them, which are often costly and time-consuming.

Application of Electroencephalography (EEG) in Ergonomics: A Systematic Review
Seyed Abolfazl Zakerian, - Bahram Kouhnavard,
Volume 9, Issue 3 (12-2021)
Abstract
Background and Objectives: Electroencephalography is one of the non-invasive and relatively inexpensive methods that can be used to evaluate neurophysiology and cognitive functions. This systematic review study was performed with the aim of using electroencephalography (EEG) in ergonomics.
Methods: In this review study, all articles published in Persian and English on the application of electroencephalography (EEG) in ergonomics from March 20, 2010 to March 21, 2021 were reviewed. For this purpose, a systematic search of articles was performed using the keywords cognitive ergonomics, mental fatigue, electroencephalography, EEG and brain waves in the databases of PubMed, Google Scholar, Web of science, SID, Scopus, Magiran Iran Medex.
Results: Most studies were conducted between 2015 and 2020 (41 papers) and most of the subjects were car drivers. Selected articles were reviewed in seven areas of mental fatigue, mental workload, mental effort, visual fatigue, working memory load, emotions, stress, and error diagnosis. The journal Perceptual and Motor Skills, followed by Applied Ergonomics, published the largest number of related articles.
Conclusion: In the reviewed articles, the assessment of a person's mental states, especially when driving a vehicle, has been further studied and through it, tracking, monitoring and various tasks of working memory have been followed. Future research should focus on the use of computational methods that take into account the dynamic and unstable nature of EEG data. Such an approach could facilitate the development of fatigue detection systems and automated adaptive systems.

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