In an era dominated by a knowledge-based economy reliant on optimal technological utilization, the emerging field of neuroergonomics is poised to exert a growing influence on both workplace and individual productivity. Neuroergonomics, characterized by its focus on advanced neurotechnology, enhancing human-computer interaction, refining the efficiency and safety of human-machine collaboration, expediting training and skill acquisition, facilitating neurorehabiltation, and promoting neurocognitive enhancement, holds vast potential for real-time applications within authentic work environments.1–6 As contemporary developed economies grapple with a decline in workplace productivity, there is escalating pressure to sustain economic growth through diverse means, including the augmentation of workers’ cognitive capacities. This transformation is now within reach due to the advancement of noninvasive, miniaturized, battery-operated, portable, wearable, mobile, and wireless brain monitoring technologies, such as electroencephalography (EEG)7 and functional near-infrared spectroscopy (fNIRS),8 alongside noninvasive neurostimulation technologies such as transcranial electrical stimulation with direct current (tES/tDCS),9 transcranial magnetic stimulation (TMS),10 and most recently focused ultrasound.11 The evolution of these neurotechnologies hints at a future where the role of the brain in the workplace could undergo profound and potentially transformative changes, contingent upon judicious decisions regarding their utilization.12 This dynamic landscape opens avenues for both beneficial and detrimental outcomes, underscoring the pivotal role of responsible technology deployment in shaping the future of cognitive work.

NEUROERGONOMIC TECHNOLOGY FOR THE WORKPLACE

Until recently, the monitoring and stimulation of brain activity with high spatial and temporal resolution necessitated invasive techniques conducted within tightly controlled clinical or experimental laboratory environments. However, these methods carried inherent restrictions and risks and were impractical for deployment in real-world scenarios, such as workplace settings. Recent strides in neuroengineering technology have ushered in noninvasive, minimally intrusive, safe, and miniaturized systems and methods, and hence come with minimal risk, and are adaptable to real-world situations through the use of portable and miniaturized devices,12,13 giving rise to the burgeoning field of neuroergonomics in real-world settings.14–19 This innovative field not only facilitates the prevention and treatment of brain pathologies but also holds the promise of enhancing normal brain function. Its applications span a multitude of everyday experiences encompassing medical practice, classrooms, military operations, legal systems, marketplaces, athletic endeavors, and the broader workplace landscape.20

Within the realm of neuroergonomics, particularly in the workplace and beyond, two primary approaches can be employed: monitoring of brain function and the alteration of cognitive function, as well as a combination of both to establish a closed-loop system to assess, adapt, and individualize to ensure or sustain the desired outcomes. The desired outcome in this case is optimal neural efficiency as defined below assessed for each individual user and each individual task. The neural underpinnings of cognitive function monitoring find optimal characterization through wearable neuroimaging, fNIRS and EEG, either in isolation or coupled with each other or physiological function monitoring modalities (eg, heart rate, dermal conductivity, pupillometry, ultrasonography, etc). EEG has been developed and utilized for almost a century with continuous improvements and innovation in methodology such as digitization, wireless, dry-electrode, around-ear, etc in hardware,21,22 and new signal processing and machine learning approaches in software.23,24 Electroencephalography measures neural activity as scalp voltage fluctuations and can capture continuous brain waves, or oscillations with power at various frequency bands to indicate mental states, or event-related potentials, in response to external stimuli. In contrast, optical brain activity monitoring stands out as the most recently developed neuroimaging method, swiftly evolving into a widely embraced research tool over the past few decades.8,25–27 Focused on measuring localized brain activity, fNIRS has been widely deployed to assess cognitive effort during task performance via cortical oxygen consumption in a specific brain region by analyzing the changes in oxygenated and deoxygenated hemoglobin via infrared spectroscopy. These represent the cortical hemodynamic response, similar to functional magnetic resonance imaging’s Blood Oxygen Level Dependent (BOLD) signal, but using wearable and miniaturized sensors. When coupled with behavioral performance indicators such as task accuracy or duration, this brain activation from task response areas has been shown to represent a more comprehensive measure of neural efficiency. Specifically, neural efficiency (NE) is determined as the difference between the behavioral efficiency of task performance (P) and cognitive effort of brain activation (CE) as shown in Figure 1.28

FIGURE 1:

Neural efficiency can be calculated from the difference between behavioral efficiency of task performance (P) and cognitive effort or brain activation (CE).

With the ability to monitor the neural efficiency of tasks in real-time within real-world settings like the workplace, the natural question arises: can this efficiency be enhanced? Evidence suggests that transcranial direct brain stimulation delivered through techniques like tES/tDCS and TMS can indeed achieve this.29,30 Additionally, focused ultrasound holds promise in this realm to combine strengths of tES/tDCS and TMS with higher spatial resolution for targeting brain areas via miniaturized form factor, albeit with a current emphasis on therapeutic rather than workplace applications.11 Transcranial magnetic stimulation is typically administered repetitively with multiple pulses and at approximately 1 Hz over short durations such as 15 minutes. Similarly, tDCS is generally applied at low intensities of 1 to 2 mA for brief periods ranging from 5 to 20 minutes.31,32 Simultaneous use of neurostimulation with neuroimaging is another emerging research area that allows further refinement of the overall approaches.33 Both TMS34 and tDCS35 can be used simultaneously with neuroimaging. Neuroimaging and neurostimulation are summarized in Figure 2.

FIGURE 2:

Wearable neurotechnologies can record or alter brain activity signals. EEG and fNIRS can be used to capture different types of neurophysiological signals. TMS and tES/tDCS can be used to excite or inhibit neural activity. Task related brain activity and task performance can be used to calculate neural efficiency as a composite index.

NEUROERGONOMIC APPLICATIONS IN THE WORKPLACE

Wearable neuroimaging has become an invaluable research tool for monitoring brain activity during skill acquisition and the execution of complex tasks36–39 in a way that in many instances is better than available options, as discussed below. This advancement holds potential advantages such as providing improved accommodations for neurodivergent employees within the workplace. Furthermore, the monitoring of neural efficiency plays a critical role in safety-sensitive or security-sensitive work situations. For instance, a study focusing on subjects engaged in a 30-minute sustained attention reaction time task, monitored using fNIRS over the prefrontal and right parietal areas, demonstrated significant differences between the initial and final 10 minutes.40 Along with many other studies, this underscores the feasibility of cognitive tracking using fNIRS and highlights its potential application in tasks requiring sustained attention susceptible to compromise by distractions or multitasking.40

The utility of fNIRS extends to distinguishing performance disparities between artificial training exercises and real-life situations. For instance, monitoring pilots during flight simulation versus actual flight revealed higher anterior prefrontal cortex activation and increased errors in real-flight conditions during cognitively demanding tasks. This emphasizes the nuanced differences in cognitive demands between simulated and authentic scenarios.41 Moreover, fNIRS proves beneficial in assessing cognitive readiness for employees returning to work after experiencing mental disorders. A study evaluating the prefrontal cortex in return-to-work trainees in remission from depression, utilizing fNIRS, successfully differentiated those in a healthy state, surpassing the accuracy of a standard profile of mood questionnaire.42

Additionally, the compatibility of workers and their workplace can be evaluated through fNIRS, informing better workplace design. A study incorporating fNIRS monitoring of the prefrontal cortex activation during an imaging stress task in ergonomic and nonergonomic workstations revealed a positive correlation between activation in the ergonomic workstation and improved task performance. This was coupled with decreased salivary alpha-amylase activity compared to the nonergonomic workstation, emphasizing the potential of fNIRS in guiding ergonomic workplace design.43

In addition, noninvasive transcranial brain stimulation technologies prove effective in augmenting human performance during complex tasks, including work-related activities.44 For instance, tDCS applied to the dorsolateral prefrontal cortex during a vigilance task demonstrated a significant improvement in target detection performance, showcasing its potential to mitigate performance degradation in settings requiring sustained attention.45 Transcranial brain stimulation also exhibits promise in enhancing learning and cognitive functioning. Studies have shown that tDCS during skill acquisition of cognitive training tasks can lead to immediate improvements in performance, persisting even on the following day.46 This extends to motor skills with tDCS significantly improving motor learning and fine motor task performance, suggesting applications in athletic, military, and other training.47 Indeed, the application of tDCS in various surgical skills training, military tasks, and athletic performance has garnered support from multiple studies, underscoring its versatility and potential across diverse occupational domains including laparoscopy, neurosurgery, and robotic surgery.48–53

Finally, the integration of neuroimaging with transcranial brain stimulation offers a comprehensive approach, allowing for direct monitoring of interventions to optimize their effects.34,35,54 For instance, fNIRS was employed to evaluate the neural efficiency of subjects during a speed-of-processing task with and without TMS, demonstrating a significant enhancement of neural efficiency during neurostimulation.29 This integrated approach holds promise in tailoring interventions for maximal impact in various cognitive and task-oriented domains as well as clinical applications.33

NEUROERGONOMICS AND WORKPLACE PRODUCTIVITY

There is extensive discussion in the occupational health literature on the relationship between worker health and productivity.55 Given the above discussion, neuroergonomics seems to add a whole new dimension to this discussion which may be particularly timely given an expanding role for cognitive functioning in a knowledge-based economy combined with a chronic long-term decline in overall workplace productivity. Overall productivity increases over time have been driven by population growth and technological innovations.56 Population growth has been slowing and is projected to turn negative in the near future, and countries already in this situation have been unsuccessful in reversing declines. The types of major technological breakthroughs that propelled increasing productivity throughout the Industrial Revolution have not occurred in recent decades, although, of course, it is hard to predict when they might occur; artificial intelligence might be one candidate, but it has not shown that kind of effect yet. Rather than getting increased productivity from more people or technology, it has been suggested that we make the workforce innately more productive, eg, cloning humans of extraordinary intelligence or genetically engineering them with such abilities. These technologies are theoretically available but generally deemed to be unlikely to be employed anytime soon, if ever, for practical and ethical reasons.56 However, the neuroergonomic approaches discussed above are already available and seem relatively benign compared to the other alternatives. Thus, the question arises should they be used and who should decide?

It has been recognized for some time that employers will have a great incentive to induce employees to enhance their neurocognitive work abilities well beyond their normal range.57 This is already happening. For example, many employers currently require computer-based augmentation of their employees’ capabilities and offer caffeine stimulation.20 If neurotechnologies prove to be even better at improving capabilities, their use may conceivably become an implicit or explicit job requirement. This has prompted suggestions that societal remedies may be needed to prevent coercion and discrimination by employers and insurers against workers who prefer not to comply.57 At the same time, it has been noted that such actions should not necessarily prevent workers from freely pursuing these options for self-improvement.57,58 After all, don’t we generally view self-improvement as a laudable goal? We already know for many people enhancement of neurocognitive function by pharmacological methods is regularly practiced, for example, to improve educational performance. Thus, in the workplace, overt coercion may be entirely unnecessary. As has been pointed out, competition with enhanced coworkers will likely produce strong incentives for other workers to engage in similar enhancement to keep up.58 It is difficult to see how regulatory action could address this aspect of the issue. In addition, the incentives to participate may be more explicitly transactional. For example, it has been suggested that instead of offering work contracts based on standard working hours, employers may offer contracts specifying effort based on employees’ cognitive workload and that this will shortly be necessary and commonplace as artificial intelligence applications become widely used in manufacturing systems rather than “replacing” human workers.59 How should we balance these competing ideas?

THE ETHICS OF NEUROENHANCEMENT AND THE ENHANCEMENT OF NEUROETHICS

Debate over the ethics of human enhancement of all kinds (for work or otherwise, of the brain or otherwise) has a long history.60 The perceived optimal response or nonresponse to this issue hinges on one’s ethical perspective. There are reasonable ethical arguments to be made both in favor and against the use of enhancement. For example, arguments proenhancement besides the autonomous rights of individuals to pursue self-improvement noted above include overcoming traditional human limitations for the benefit of society. On the other hand, arguments antienhancement besides the potential coercive application noted above include an unnatural threat to the human essence, entrenched inegalitarianism that could increase global inequity and socioeconomic divides and undermine democracy, and the risk of unknown future harm that would exceed any short-term benefits.57 Given these apparently valid arguments on both sides of the issue, it seems that our current moral reasoning capacity is not capable of resolving the issue. Thus, is it possible that our moral reasoning needs enhancement and can be enhanced?

It has been noted with regard to other pressing societal problems, such as addressing global environmental injustice including climate change, that our evolved state of moral reasoning is constrained by an evolutionary mismatch between our Paleolithic genome and our postmodern society.61 Thus, it has been suggested that augmented neurocognition through manipulation of brain circuitry could enhance moral reasoning to address the problem. For example, studies have demonstrated that noninvasive tDCS of the left dorsolateral prefrontal cortex shifts the preference of moral judgments to the nonutilitarian.62 A review of other studies showed that this approach can have the effects of increasing altruism, trust, cooperation, fairness, empathy, and other prosocial behaviors such as an enhanced willingness to intervene to help others.63 Thus ultimately, we may be in the ironic position that to reach the best solution to the ethical issues of neurotechnologies (and other vexing societal problems) we will have to use the same neurotechnologies to enhance our moral reasoning and neuroethics.

CONCLUSIONS

Because the presumed application of neuroergonomic enhancement in the workplace is likely more imminent than the realization of an enhanced moral reasoning, is a societal response needed, eg, in the form of regulatory controls? Just as genetic engineering will allow us to decode and alter the genome of our cells, so similarly neuroengineering aims to decode and alter the connectome of our brains. Along those lines, it has been noted that data obtained by employers from brain-interfacing devices of employees may not be considered medical or health data and thus may not qualify for protections by current laws such as the US Health Insurance Portability and Accountability Act (HIPAA) or the European General Data Protection Regulation so additional privacy regulations might be needed.20 It has also been suggested that legislation analogous to the Genetic Information Nondiscrimination Act of 2008 (GINA), which protects workers from genetic discrimination in employment, should be considered to protect workers from discrimination against refusal to participate in neuroergonomic enhancement.57 There is some logic to these suggestions since brain activity data are arguably a more interpretable and intimate representation of who a person is than their genome.20 However, public opinion might differ on this issue. For example, a recent survey of the general public in Japan presented individuals with four scenarios involving minimally to highly invasive neurological enhancement interventions. Only about 20% of respondents stated a willingness to use enhancement technologies, especially if they were less invasive, and 80% were not. On the other hand, about 80% of respondents were tolerant toward others’ use of such technologies, and about 80% felt that there was no need for banning such technologies.64 This outlook would be consistent with many legal systems that are structured to allow users to choose to take calculated risk. Of course, this presupposes that users will be aware of any potential risks and how the technology should be used properly. Thus, it may be necessary to require manufacturers and suppliers of such devices to fully inform potential users of the appropriate use constraints and to clearly disclose any possible adverse effects, including long-term use risks, which unfortunately are currently poorly understood.20 Further study of attitudes among various stakeholder groups might be revealing, as has been done for employees’ and occupational health professionals’ perspectives on the use of genetic testing in the workplace.65,66

Even so, additional considerations for issues of workplace applications seem warranted. Perhaps at the very least, relevant stakeholders and individuals with expertise in dealing with such situations (including engineers, occupational health professionals, employees, employers, lawyers, ethicists, social scientists, government officials, etc) need to be convened to consider what applications would be considered acceptable and which should be avoided. For example, recently the Institute of Electrical and Electronics Engineers Standard Association put together a standards roadmap whitepaper on neurotechnologies for brain-computer interfacing.67 This whitepaper called for a working group on the Recommended Practice for the Responsible Design and Development of Neurotechnologies. They would be tasked “to assess the ethical and socio-technical considerations and practices regarding the design, development, and use of neurotechnologies…which enables developers, researchers, users and regulators to anticipate and address ethical and sociocultural implications of neurotechnologies, mitigating negative unintended consequences, while increasing community support and engagement with neurotechnology innovators.”68 Similarly, occupational health organizations need to examine their ethical standards to ensure that practicing professionals will be prepared to advise employers on the application of neuroergonomics in their workplaces in a way that is best for the health and safety of their workers. It would seem that these steps are “no-brainers” requiring no further cognitive enhancement to implement.

REFERENCES 1. Ayaz H, Dehais F. Neuroergonomics: The Brain at Work and Everyday Life. Cambridge: Elsevier Academic Press; 2019. 2. Ayaz H, Dehais F. Neuroergonomics. In: Salvendy G, Karwowski W, eds. Handbook of Human Factors and Ergonomics. 5th ed. Hoboken, NJ: Wiley; 2021:816–841. 3. Dehais F, Karwowski W, Ayaz H. Brain at work and in everyday life as the next frontier: grand field challenges for neuroergonomics. Front Neuroergonomics 2020;1:583733. Available at: https://doi:org/10.3389/fnrgo.2020.583733. 4. Parasuraman R. Neuroergonomics: brain, cognition, and performance at work. Curr Dir Psychol Sci 2011;20:181–186. 5. Parasuraman R, Christensen J, Grafton S. Neuroergonomics: the brain in action and at work. Neuroimage 2012;59:1–3. 6. Parasuraman R, Rizzo M. Neuroergonomics: The Brain at Work. New York: Oxford University Press; 2007. 7. Gramm K, Ferris DP, Gwin J, et al. Imaging natural cognition in action. Int J Psychophysiol 2014;91:22–29. 8. Ayaz H, Baker WB, Blaney G, et al. Optical imaging and spectroscopy for the study of the human brain: status report. Neurophotonics 2022;9(suppl 2):S24001. Available at: https://doi:org/10.1117/1.NPh.9.S2.S24001. 9. Bikson M, Hanlon CA, Woods AJ, et al. Guidelines for TMS/tES clinical services and research through the COVID-19 pandemic. Brain Stimul 2020;13:1124–1149. Available at: https//:doi.org/10.1016/j.brs.2020.05.010. 10. Ekhtari H, Tavakoli H, Addolorato G, et al. Transcranial electrical and magnetic stimulation (tES and TMS) for addiction medicine: a consensus paper on the present state of the science and the road ahead. Neurosci Biobehav Rev 2019;104:118–140. Available at: https://doi.org/10.1016/j.neubiorev.2019.06.007. 11. Kubanek J. Neuromodulation with transcranial focused ultrasound. Neurosurg Focus 2018;44:E14. Available at: https://doi.org/10.3171/2017.11.FOCUS17621. 12. Roelfsema PR, Denys D, Klink PC. Mind reading and writing: the future of neurotechnology. Trends Cogn Sci 2018;22:598–610. Available at: https://doi.org/10.1016/j.tis.2018.04.001. 13. Fairclough SH, Lotte F. Grand challenges in neurotechnology and system neuroergonomics. Front Neuroergonomics 2020;1:602504. Available at: https://doi.org/10.3389/fnrgo.2020.602504. 14. Gramann K, Fairclough SH, et al; Zander TO. Editorial: trends in neuroergonomics. Front Hum Neurosci. 2017;11:165. Available at: https://doi.org/10.3389/fnhum. 15. Gramann K, McKendrick R, Baldwin C, et al. Grand field challenges for cognitive neuroergonomics in the coming decades. Front Neuroergonomics 2021;2:643969. Available at: https://doi.org/10.3389/fnrgo.2021.643969. 16. Hancock PA, et al. Front Hum Neurosci 2019;13:115. Available at: https://doi.org/10.3389/fnhum.2019.00115. 17. Krueger F, Weiss E. Specialty grand challenge article—social neuroergonomics. Front Neuroergonomics 2021;2:654597. Available at: https://doi.org/10.3389/fnrgo.2021.654597. 18. Perrey S. Grand challenges in physical neuroergonomics. Front Neuroergonomics 2023;4:1137854. Available at: https://doi.org/10.3389/fnrgo.2023.1137854. 19. Stangl M, Maoz SL, Suthana N. Mobile cognition: imaging the human brain in the ‘real world’. Nat Rev Neurosci 2023;26:347–362. 20. Gaudry KS, Ayaz H, Bedows A, et al. Projections and the potential societal impact of the future of neurotechnologies. Front Neuroergonomics 2021;15:658930. Available at: https://doi.org/10.3389/frins.2021.658930. 21. Kappel SL, Rank M, Toft HO, et al. Dry-contact electrode ear-EEG. IEEE Trans Biomed Eng 2019;66:150–158. 22. Niso G, Romero E, Moreau JT, et al. Wireless EEG: a survey of systems and studies. Neuroimage 2023;269:119774. Available at: https://doi.org/10.1016/j.neuroimage.2022.119774. 23. Fabien L, Laurent B, Andrzej C, et al. A review of classification algorithms for EEG- based brain-computer interfaces: a 10-year update. J Neural Eng 2018;15:031005. Available at: https://doi.org/10.1088/1741-2552/aab2f2. 24. Kawala-Sterniuk A, Browarska N, Al-Bakri A, et al. Summary of over fifty years of brain-computer interfaces – a review. Brain Sci 2021;11:43. Available at: https://doi.org/10.3390/brainsci11010043. 25. Ferrari M, Quaresima V. A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application. Neuroimage 2012;63:921–935. 26. Izzetoglu M, Izzetoglu K, Bunce S, et al. Functional near-infrared neuroimaging. IEEE Trans Neural Syst Rehabil Eng 2005;13:153–159. 27. Yucel M, Luhmann A, Scholkmann F, et al. Best practices for fNIRS publications. Neurophotonics 2021;8:012101. Available at: https://doi.org/10.1117/1.NPh.8.012101. 28. Curtin A, Ayaz H. Neural Efficiency Metrics in Neuroergonomics: Theory and Applications. In: Ayaz H, Dehais F, eds. Neuroergonomics. Cambridge: Academic Press; 2019;133–140. 29. Curtin A, Ayaz H, Tang Y, et al. Enhancing neural efficiency of cognitive processing speed via training and neurostimulation: an fNIRS and TMS study. Neuroimage 2019;198:73–82. Available at; https://doi.org/10.106/j.neuroimage.2019.05.020. 30. McKendrick R, Falcone B, Scheldrup M, et al. Effects of transcranial direct current stimulation on baseline and slope of prefrontal cortex hemodynamics during a special working memory task. Front Hum Neurosci 2020;14:64. Available at: https://doi.org/10.3389/fnhum.2020.00064. 31. Woods AJ, Antal A, Bilson M, et al. A technical guide to tDCS and related non-invasive brain stimulation tools. Clin Neurophysiol 2016;127:1031–1048. 32. Antal A, Luber B, Brem AK, et al. Non-invasive brain stimulation and enhancement. Clin Neurophysiol Pract 2022;25:1230–1255. 33. Teo WP, Muthalib M, Yamin S, et al. Does a combination of virtual reality, neuromodulation and neuroimaging provide a comprehensive platform for neurorehabilitation? – a narrative review of the literature. Front Hum Neurosci 2016;10:284. Available at: https://doi.org/10.3389/fnhum.2016.00284. 34. Curtin A, Tong S, Sun J, et al. A systematic review of integrated functional near-infrared spectroscopy (fNIRS) and transcranial magnetic stimulation (TMS) studies. Front Neurosci 2019;13:84. Available at: https://doi.org/10.3389/fnins.2019.00084. 35. Patel R, Dawidziuk A, Singh H, et al. Systematic review of combined functional near- infrared spectroscopy and transcranial direct-current stimulation studies. Neurophotonics 2020;7:020901. Available at: https://doi.org/10.1117/1.NPh.7.2.020901. 36. Ayaz H, Onaral B, Izzetoglu K, et al. Continuous monitoring of brain dynamics with functional near infrared spectroscopy as a tool for neuroergonomic research: empirical examples and a technological development. Front Hum Neurosci 2013;7:871. Available at: https://doi.org/10.3389/fnhum.2013.00871. 37. Finn ES, Glerean E, Hasson U, et al. Naturalistic imaging: the use of ecologically valid conditions to study brain function. Neuroimage 2022;247:118776. Available at: https://doi.org/10.1016/j.neuroimage.2021.118776. 38. Mehta RK, Parasuraman R. Neuroergonomics: a review of applications to physical and cognitive work. Front Hum Neurosci 2013;7:889. Available at; https://doi.org/10.3389/fnhum.2013.00889. 39. Wascher E, Reiser J, Rinkenauer G, et al. Neuroergonomics on the go: an evaluation of the potential of mobile EEG for workplace assessment and design. Hum Factors 2023;65:86–106. Available at: https://doi.org/10.1177/00187208211007707. 40. Derosiere G, Dalhoumi S, Perry S, et al. Towards a near infrared spectroscopy-based estimation of operator attentional space. PLoS One 2014;9:e92045. Available at: https://doi.org/10.1371/journal.pome.0092045. 41. Gateau T, Ayaz H, Dehais F. In silico vs over the clouds: on-the-fly mental state estimation of aircraft pilots, using a functional near infrared spectroscopy base passive- BCI. Front hum Neurosci 2018;12:187. Available at: https://doi.org/10.3389/fnhum.2018.00187. 42. Atsumori H, Obata AN, Sato H, et al. Prefrontal transcranial activation of return-to-work trainees in remission of mental disorders with depressive symptoms compared to that of healthy controls. J Biomed Optics 2019;24:1–7. Available at; https://doi.org/10.1117/1.JBO.24.5.056008. 43. Alyan E, Saad NM, Kamel N. Effects of workstation type on mental stress: FNIRS study. Hum Factors 2021;63:1230–1255. 44. McKinley RA, Bridges N, Walters CM, et al. Modulating the brain at work using noninvasive transcranial stimulation. Neuroimage 2012;59:129–137. 45. Nelson JT, McKinley RA, Golob EJ, et al. Enhancing vigilance in operators with prefrontal cortex transcranial direct current stimulation (tDCS). Neuroimage 2014;85:909–917. 46. Martin DM, Liu R, Alonzo A, et al. Use of transcranial direct current stimulation (tDCS) to enhance cognitive training: effect of timing of stimulation. Exp Brain Res 2014;232:3345–3351. 47. Wilson MA, Greenwell D, Meek AW, et al. Neuroenhancement of a dexterous motor task with anodal tDCS. Brain Res 2022;1790:147993. Available at: https://doi.org/10.1016/j.brainres.2022.147993. 48. Ashcroft J, Patel R, Woods AJ, et al. Prefrontal transcranial direct-current stimulation improves early technical skills in surgery. Brain Stimulat 2020;13:1834–1841. 49. Ciechanski P, Kirton A, Wilson B, et al. Electroencephalography correlates of direct-current stimulation enhanced surgical skill learning: a replication and extension study. Brain Res 2019;1725:146445. Available at: https://doi.org/10.1016/j.brainres.2019.146445. 50. Colzato LS, Mitsche MA, Kibele A. Noninvasive brain stimulation and neural entrainment enhance athletic performance – a review. J Cogn Enhanc 2017;1:73–79. 51. Cox ML, Deng Z-D, Palmer H, et al. Utilizing transcranial direct current stimulation to enhance laparoscopic technical skills training: a randomized control trial. Brain Stimul 2020;13:863–872. 52. Feltman KA, Hayes AM, Bernhardt KA, et al. Viability of tDCS in military environments for performance enhancement: a systematic review. Mil Med 2020;185:e53–e60. 53. Patel R, Suwa Y, Kinross J, et al. Neuroenhancement of surgeons during robotic suturing. Surg Endosc 2022;36:4803–4814. 54. Esmaielpour Z, Shereen AD, Ghobadi-Azbari P, et al. Methodology for tDCS integration with fMRI. Hum Brain Mapp 2020;41:1950–1957. 55. Bevan S, Cooper CL. The Healthy Workforce: Enhancing Wellbeing and Productivity in the Workers of the Future. Leeds, UK: Emerald Group Publishing; 2021. 56. MacAskill W. What We Owe the Future. New York: Basic Books; 2022. 57. Appel JM. When the boss turns pusher: a proposal for employee protections in the age of cosmetic neurology. J Med Ethics 2008;34:616–618. 58. Farah MJ, Illes J, Cook-Deegan R, et al. Neurocognitive enhancement: what can we do and what should we do? Nat Rev Neurosci 2004;5:421–425. 59. Acarturk C, Mucen B. Performance in the workplace: a critical evaluation of cognitive neuroenhancement. NanoEthics 2022;16:107–114. 60. Harris PA. Enhancing Evolution: The Ethical Case for Making Better People. Princeton: Princeton University Press; 2010. 61. Brandt-Rauf PW. Environmental equity and evolutionary engineering: our ecological footprints and our ethical footprints. Significances Bioeng Biosci 2022. Available at: https://doi.org/10.31031/SBB.2022.05.000619. 62. Kuehne M, Heimrath K, Heinze H-J, et al. Transcranial direct current stimulation of the left dorsolateral prefrontal cortex shifts preference of moral judgments. 2015;10:PLoS One, e0127061. Available at: https://doi.org/10.1371/journal.pone.0127061. 63. Darby RR, Pascual-Leone A. Moral enhancement using non-invasive brain stimulation. Front Hum Neurosci 2017;11:77. Available at: https://doi.org/10.3389/fnhum.2017.00077. 64. Nakazawa E, Mori K, Udagawa M, et al. A cross-sectional study of attitudes toward willingness to use enhancement technologies: implications for technology regulation and ethics. BioTech (Basel) 2022;11:21. Available at: https://doi.org/10.3390/biotech11030021. 65. Brandt-Rauf SI, Brandt-Rauf E, Gershon R, et al. The differing perspectives of workers and occupational medicine physicians on the ethical, legal and social issue of genetic testing in the workplace. New Solut 2011;21:89–102. 66. Brandt-Rauf SI, Brandt-Rauf E, Gershon R, et al. Genes, jobs, and justice: occupational medicine physicians and the ethical, legal, and social issues of genetic testing in the workplace. Ethics & Medicine 2011;27:51–61. 67. IEEE Standards Association. Standards roadmap: neurotechnologies for brain-machine interfacing. 2020. Available at: https://standards.ieee.org/wp-content/uploads/documents/presentations/ieee-neurotech-for-bmi-standards-roadmap.pdf. Accessed February 20, 2020. 68. IEEE Standards association. Recommended practice for the responsible design of neurotechnologies 2022. Available at: https://standards.ieee.org/ieee/7700/11038/. Accessed September 21, 2022.