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Special Issue "Current Perspectives on Neuroergonomics"

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A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Systems Neuroscience".

Deadline for manuscript submissions: closed (25 March 2021) | Viewed by 8536

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Special Issue Editors

Prof. Dr. Edmund Wascher

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Guest Editor

Leibniz Research Centre for Working Environment and Human Factors (IfADo), TU Dortmund University, 44139 Dortmund, Germany
Interests: human-machine systems; adaptive interfaces; perception-action integration; visual attention; neurophysiology of cognitive processing
Special Issues, Collections and Topics in MDPI journals

Dr. Stefan Arnau
E-Mail Website
Co-Guest Editor

Leibniz Research Centre for Working Environment and Human Factors (IfADo), TU Dortmund University, Dortmund 44139, Germany
Interests: EEG; cognitive resources; effort; task engagement; mental fatigue; mind wandering; motivation

Dr. Thorsten Plewan

E-Mail Website
Co-Guest Editor

1. Leibniz Research Centre for Working Environment and Human Factors (IfADo), TU Dortmund University, Dortmund 44139, Germany
2. Hochschule Fresenius, University of Applied Scienes, Psychology School, Düsseldorf 40476, Germany
Interests: attention; cognitive ergonomics; human–machine interaction; visual information processing; virtual and augmented reality applications

Special Issue Information

Dear Colleagues,

Continuous information processing and mental load play an increasingly important role in modern working environments. Until recently, the investigation of task demands with neuroscientific methods had been restricted to laboratory settings. Advances in technologies like mobile EEG or mobile fNIRS, however, now allow for measuring cognitive load in real-life working environments or in simulations that are close to it. In this way, new possibilities emerge to track cognitive processing. These possibilities enable us to re-design working environments and potentially reduce cognitive load. Such ergonomically designed work places could be beneficial for the workers’ health and well-being directly and indirectly, as the tracking of mental load could also be used to increase safety, especially in monotonous monitoring activities or piloting.

This Special Issue is dedicated to studies on work-related applications of neurocognitive methods. Studies on mental states as well as information processing at work are of interest, as are studies reporting on the development of methods relevant for the field.

Prof. Dr. Edmund Wascher
Dr. Stefan Arnau
Dr. Thorsten Plewan
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

Cognitive ergonomics
Mental states
Mental load
Workplace
Mobile psychophysiology

Published Papers (4 papers)

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Research

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Open AccessArticle

Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

and

Brain Sci. 2021, 11(7), 885; https://doi.org/10.3390/brainsci11070885 - 30 Jun 2021

Cited by 4 | Viewed by 2130

Abstract

The nature of firefighters’ duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees’ safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of

0.844

and an accuracy of

79.10 %

if the training and testing data are obtained at similar environmental conditions. However, the algorithm’s performance dropped to an F-1 score of

0.723

and accuracy of

60.61 %

when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively. Full article

(This article belongs to the Special Issue Current Perspectives on Neuroergonomics)

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Open AccessEditor’s ChoiceArticle

Influence of Increasing Task Complexity and Use of Informational Assistance Systems on Mental Workload

Dominic Bläsing

and

Manfred Bornewasser

Brain Sci. 2021, 11(1), 102; https://doi.org/10.3390/brainsci11010102 - 14 Jan 2021

Cited by 12 | Viewed by 2243

Abstract

(1) Background: Cognitive aspects and complexity in modern manual mixed model assembly are increasing. To reduce mental workload (MWL), informational assistance systems are introduced. The influence of complexity and used assistance system on MWL should be investigated to further improve the implementation of such assistance systems. (2) Methods: Using a simulated close to real-life assembly task a 2 × 3 design was chosen, with two levels of assembly complexity (within subjects) and three different assistance systems (paper, Augmented Reality (AR)-glasses, tablet–between subjects). MWL was measured using either physiological response (electrocardiogram (ECG) and eye-tracking) or performance indicators. (3) Results: An influence of task complexity on MWL can be shown. Additionally, usability based differences between the used assistance systems become more evident with reference to the results of area of interest analysis. (4) Conclusions: Using a multi-modal measurement approach, it is possible to detect complexity-based differences in MWL. Additional research on validity and alignment is needed to further use these for (neuro-) ergonomic considerations and recommendations. Full article

(This article belongs to the Special Issue Current Perspectives on Neuroergonomics)

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Open AccessArticle

Oscillatory EEG Signatures of Affective Processes during Interaction with Adaptive Computer Systems

and

Brain Sci. 2021, 11(1), 35; https://doi.org/10.3390/brainsci11010035 - 31 Dec 2020

Cited by 2 | Viewed by 1501

Abstract

Affect monitoring is being discussed as a novel strategy to make adaptive systems more user-oriented. Basic knowledge about oscillatory processes and functional connectivity underlying affect during naturalistic human–computer interactions (HCI) is, however, scarce. This study assessed local oscillatory power entrainment and distributed functional connectivity in a close-to-naturalistic HCI-paradigm. Sixteen participants interacted with a simulated assistance system which deliberately evoked positive (supporting goal-achievement) and negative (impeding goal-achievement) affective reactions. Electroencephalography (EEG) was used to examine the reactivity of the cortical system during the interaction by studying both event-related (de-)synchronization (ERD/ERS) and event-related functional coupling of cortical networks towards system-initiated assistance. Significantly higher α-band and β-band ERD in centro-parietal and parieto-occipital regions and β-band ERD in bi-lateral fronto-central regions were observed during impeding system behavior. Supportive system behavior activated significantly higher γ-band ERS in bi-hemispheric parietal-occipital regions. This was accompanied by functional coupling of remote β-band and γ-band activity in the medial frontal, left fronto-central and parietal regions, respectively. Our findings identify oscillatory signatures of positive and negative affective processes as reactions to system-initiated assistance. The findings contribute to the development of EEG-based neuroadaptive assistance loops by suggesting a non-obtrusive method for monitoring affect in HCI. Full article

(This article belongs to the Special Issue Current Perspectives on Neuroergonomics)

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Review

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Open AccessReview

Neuroergonomics: A Perspective from Neuropsychology, with a Proposal about Workload

David J. Hardy

Brain Sci. 2021, 11(5), 647; https://doi.org/10.3390/brainsci11050647 - 15 May 2021

Cited by 2 | Viewed by 1803

Abstract

In a brief overview of neuroergonomics, including some personal reminiscences of Raja Parasuraman, it is recognized that the field of human factors and ergonomics has benefitted greatly from the inclusion and integration of neuroscientific methods and theory. It is argued that such synergistic success can work in the other direction as well with the inclusion of methods and theory of human factors by a neuro field, in this case, neuropsychology. More specifically, it is proposed that neuropsychology can benefit from the inclusion of workload measures and theory. Preliminary studies on older adults, persons living with HIV, and patients with a traumatic brain injury or multiple sclerosis, are reviewed. As an adjunct measure to neuropsychological tests, the construct of workload seems perfectly suited to provide an additional vector of information on patient status, capturing some of the large individual differences evident in clinical populations and facilitating the early detection of cognitive change. Full article

(This article belongs to the Special Issue Current Perspectives on Neuroergonomics)

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