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Volume 9, Issue 3 (Iranian Journal of Ergonomics 2021)                   Iran J Ergon 2021, 9(3): 1-18 | Back to browse issues page

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Zakerian S A, Kouhnavard B. Application of Electroencephalography (EEG) in Ergonomics: A Systematic Review. Iran J Ergon 2021; 9 (3) :1-18
URL: http://journal.iehfs.ir/article-1-833-en.html
1- Department of Occupational Health Engineering, School of Health, Tehran University of Medical Sciences, Tehran, Iran
2- Student’s Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran. , bahramk2011@gmail.com
Abstract:   (7048 Views)
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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Type of Study: Research | Subject: Other Cases
Received: 2021/08/1 | Accepted: 2022/01/4 | ePublished: 2022/01/30

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