by Michael Jackson O. de Andrade 

Professor de Andrade is with the Laboratory of Neuroscience, Chronobiology and Sleep Psychology, Department of Psychology, State University of Minas Gerais in Minas Gerais, Brazil.

FUNDING: No funding was provided for this study. 

DISCLOSURES: The author has no conflicts of interest relevant to the content of this article. 

Innov Clin Neurosci. 2021;18(10–12):28–29.

ABSTRACT: This article aims to identify the importance of markers at the neurophysiological, physiological, and behavioral levels of mental states linked to the performance of workers as an influence in the field of neuroscience and psychology at work. The author discusses the emergence of neuroergonomy as an area of neuroscience principles and cognitive psychology related to human factors for fundamentals of analysis in brain dynamics, mainly through the use of noninvasive tools, such as the use of electroencephalography for physiological aspects of mental exhaustion of the worker. Neuroergonomics is seen as fundamental for the investigation of complex relationships of organizations, especially when interacting with technology. Future research should involve more complex team-building scenarios and enrich the different neuroergonomic solutions.

Keywords: Neurosciences, work, ergonomics

The study of mental workload is fundamental to understanding the intrinsic limitations of the human information processing system.1 However, the field of behavioral and neurophysiological synchrony should open up promising paths for studies of human–human and human–machine interactions in organizations and work environments. From this point of view, neuroergonomics combines behavioral and cognitive phenomena related to the ways of working with the engineering of neurosciences and their neurophysiological products, resulting from analyses of the conditions in the field of work and organizations. This field of investigation is currently on the rise and seeks to explain, at conceptual levels, the workings of the worker’s brain. To this end, these considerations aim to identify the importance of markers at the neurophysiological, physiological, and behavioral level of mental states linked to the performance of workers or operators as an influence in the field of neuroscience and psychology at work.

Neuroscience is a recent and representative area in the psychology of work, and its applications tend to grow considerably.2 Ergonomics, on the other hand, is a multidisciplinary and interdisciplinary field that aims to provide and improve comfort in work environments from a health, safety, and efficiency perspective. This implies that ergonomics encompasses a psychophysiological, socioeconomic, and organizational dimension in the human–work interfaces, which evaluates measures of neuroscience in brain domains and in the context of work and organizations.3  Thus, neuroergonomics has become an area of neuroscience, cognitive psychology, ergonomics, and human factor principles in the analysis of brain dynamics, considering the intertwining between neural bases of perception and cognition that encompasses operations of undesirable states that can be continuously monitored objectively.4–6 The assessment and prediction of cognitive performance during tasks and neural observation at temporal and spatial levels can be key issues to measuring and understanding the mental workload involved in activities and functions of the social brain, such as cognitive exhaustion of the orbitofrontal and ventromedial cortex of the cerebral cortex. Recent advances in neuroergonomics have expanded our understanding of neurocognitive processes in different operational domains, and we can look for physiological and behavioral markers that explain specifically how these states are identified.1

The purpose of the neuroergonomic approach is to optimize performance by training or processing information, aiming to develop aspects related to work through neural signatures.2,7 This performance results from reliability and effective interaction between all the subsystems that make up the workload.8 In fact, neuroergonomics is the application of the study of brain functioning associated with human factors, capable of providing efficiency and safety for the development of work. This means that neuroergonomics is an agent in the construction of new methodologies for the optimization of work performance.5 Studies in the area provide an understanding of human factors at work through contextualization and characterization of different brain areas.

Electroencephalogram (EEG) techniques as a graphic record of the electrical activity of the human brain have been used in neuroergonomics.5 Authors have evaluated the workload of operators from neurological data using statistical learning methods to adjust adaptive mental work systems and to perform dynamic task allocations as workload problems arise.7 The authors used a neural network of EEG algorithms to infer the operator’s mental workload by simulation and found that neurophysiological data in future assessments of the operator’s state can be used to diagnose mental activities caused by ongoing workload. The readiness potential responses had a negative increase for prevention control and movement anticipation in supplementary motor and sensorimotor areas of the cortex, causing tension during movement preparation events. Many of the findings were perceived by brainwave velocity ratios from negative and positive potential (N400 and P300). Similarly, an adaptive model measuring the effectiveness  in operators to verify physiological adaptation has produced the best diagnostic efficacy in a variety of work domains.9 EEG alert indices include conventional measures of spectral power, as well as indices from various frequency bands, such as the task load increase rate and the low frequency engagement index for monitoring alertness during vigilance, for the early detection of loss of operator surveillance.10 In addition, the magnitude of the temporal change can be used to verify the decrease in surveillance and the performance of operator activities in real time.

Worker’s health in a 24-hour society is commonly associated with the neurobiological model of adaptation to social rhythms. Working conditions significantly influence shift and night shift tolerance. In particular, working during non-day hours can lead workers to perform worse in their tasks, expose them to greater risks of accidents at work and, more significantly, expose them to environmental stressors, which can lead to early functional incapacity. Chronobiological models in surveillance and performance tasks can increase safety and human performance, promoting working hours and countermeasures to prevent failures in performing tasks in circadian conditions that are not optimal.11 It is important to consider that the neurobiology involved in the biological clock is associated with an organization of a daytime or shift operator society, which favors the better quality of life for workers. 

Although neuroergonomic research has already achieved promising results, we still need to promote research and methods in neurosciences that understand the socioneurobiological dimensions and environments of work and workers. Individual differences between people can influence neurological measures in different ways for the same task, albeit due to the cost and difficulty of neuroimaging and electroencephalographic techniques in collecting neuroergonomic data. Being able to accurately assess an approaching future state will allow adaptive systems to intervene before worker overload occurs, thus preventing this undesirable state. In addition, the neuroergonomic system needs to be able to assess the state of the operator quickly enough to inform decisions; often, this need might require assessment in real time.8 These considerations also discuss the advancement of research in this area as being fundamental for the investigation of complex relationships of organizations, especially when the interaction with neuroimaging technology requires cognitive complexity of the dimensions of work.12 Future research should involve more complex team building scenarios and enrich the different neuroergonomic solutions.


  1. Dehais F, Lafont A, Roy R, Flairclough S. Neuroergonomics approach to mental workload, engagement and human performance. Front Neurosci. 2020;14:268.   
  2. Parasuraman R. Jiang Y. Individual differences in cognition, affect, and performance: behavioral, neuroimaging, and molecular genetic approaches. Neuroimage. 2012;59(1);70–82.
  3. Mehta RK, Parasuraman R. Neuroergonomics: a review of applications to physical and cognitive work. Front Hum Neurosci. 2013;7:1–10.
  4. Lees MN, Cosman JD, Lee JD, et al. Translating cognitive neuroscience to the driver’s operational environment: a neuroergonomics approach. Am J Psychol. 2010;123(4):391.
  5. Momin BF, Kalas MS. Study and implementation of advanced neuroergonomic techniques. Advanced Computing. 2012;3(4):9.
  6. McKinley RA, Bridges N, Walters CM, Nelson J. Modulating the brain at work using noninvasive transcranial stimulation. Neuroimage. 2012;59(1):129–137
  7. Grafton ST, Tipper CM. Decoding intention: a neuroergonomic perspective. Neuroimage. 2012;59(1):14–24.
  8. Borghetti BJ, Giametta JJ, Rusnock CF. Estimating operator workload from small participant samples. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 2016;60(1):31.
  9. Christensen JC, Estepp JR, Wilson GF, Russell CA. The effects of day-to-day variability of physiological data on operator functional state classification. Neuroimage. 2012;59(1):57–63.
  10. Kamzanova AT, Kustubayeva AM, Matthews G. Use of EEG workload indices for diagnostic monitoring of vigilance decrement. Hum Factors. 2014;56(6):1136–1149.
  11. Correa A, Molina E, Sanabria D. Effects of chronotype and time of day on the vigilance decrement during simulated driving. Accid Anal Prev. 2014;67:113–118.
  12. Liu Y, Wu C, Berman MG. Computational neuroergonomics. Neuroimage. 2012;59(1):109–116.