Neuroimaging Techniques in the Measurement of Mental Fatigue

A special issue of Behavioral Sciences (ISSN 2076-328X). This special issue belongs to the section "Experimental and Clinical Neurosciences".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 2132

Special Issue Editors


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Guest Editor
Department of Computer, Control, and Management Engineering "Antonio Ruberti", Sapienza University of Rome, 00185 Rome, Italy
Interests: cognitive neuroscience; neuroscience; signal processing; human factors

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Guest Editor
CNR-IFC, National Research Council Institute of Clinical Physiology, 56124 Pisa, Italy
Interests: cognitive neuroscience; neuroscience; neurorehabilitation; signal processing

Special Issue Information

Dear Colleagues,

Mental fatigue (MF) can be defined as a psychobiological state caused by prolonged episodes of cognitive exertion and may include feelings of sleepiness, as well as physical and mental elements. MF has already been demonstrated to decrease accuracy and increase reaction time during cognitive tasks and deteriorate behavioural and physical performance. Moreover, in real-world settings, such as the automotive, industry, aviation and health industries, MF plays a crucial role in increasing risk for accidents, injuries and/or incidents. For this reason, MF is of relevance, and despite several years of scientific research focused on the definition of MF, its objective evaluation and characterization is still an open issue. This is strictly linked to the fact that MF is a dynamic event involving subjective, mental, behavioural, neural and physiological processes that interact over time across different tasks and environmental contexts.

The use and combination of different methodologies can therefore provide a more accurate measurement of MF. For example, neurophysiological (e.g., brain activity, respiration rate, heart activity and skin conductance) signals endow a reliable way to assess and foresee MF changes as they start changing long before any external signs of fatigue. Additionally, other methodologies based on behavioural (e.g., eye tracking, posture assessment) and subjective (e.g., self-reports, debriefing) measurements can provide additional information to better assess the onset and impact of MF.

This Special Issue aims to gather a collection of studies detailing the most recent advancements in the field of MF evaluation. Authors are invited to submit cutting-edge research and reviews that address a broad range of topics related to MF. Areas covered by this section include but are not limited to the following sections:

  • Neurophysiological characterization;
  • Artificial Intelligence (AI) methodologies;
  • Multivariate autoregressive (MVAR) models;
  • Wearable technologies;
  • Human–machine interactions (HMI);
  • Human–collaborative robot (cobot) interactions.

All types of manuscripts are considered, including original basic science reports, translational research, clinical studies, review articles and methodology papers.

Dr. Gianluca Borghini
Dr. Vincenzo Ronca
Dr. Dario Rossi
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. Behavioral Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mental states
  • mental fatigue (MF)
  • Artificial Intelligence (AI)
  • multivariate autoregressive (MVAR)
  • multimodal approach
  • neuroimaging
  • neuroscience
  • decision support system (DSS)
  • neurophysiological

Published Papers (1 paper)

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Research

13 pages, 1594 KiB  
Article
What Is the Relationship between Metacognition and Mental Effort in Executive Functions? The Contribution of Neurophysiology
by Michela Balconi, Carlotta Acconito, Roberta A. Allegretta and Davide Crivelli
Behav. Sci. 2023, 13(11), 918; https://doi.org/10.3390/bs13110918 - 10 Nov 2023
Cited by 2 | Viewed by 1622
Abstract
Prolonged cognitive effort can be considered one of the core determinants of mental fatigue and may negatively affect the efficacy and efficiency of cognitive performance. Metacognition—understood as a multi-componential set of skills concerning awareness and control of one’s own cognition—might reduce such negative outcomes. [...] Read more.
Prolonged cognitive effort can be considered one of the core determinants of mental fatigue and may negatively affect the efficacy and efficiency of cognitive performance. Metacognition—understood as a multi-componential set of skills concerning awareness and control of one’s own cognition—might reduce such negative outcomes. This study aimed to explore the relation between metacognitive skills, neurocognitive performance, and the level of mental effort as mirrored by electrophysiological (EEG) markers of cognitive load and task demand. A challenging cognitive task was used to prompt and collect metacognition reports, performance data (accuracy and response times—RTs), and physiological markers of mental effort (task-related changes of spectral power for standard EEG frequency bands) via wearable EEG. Data analysis highlighted that different aspects of metacognitive skills are associated with performance as measured by, respectively, accuracy and RTs. Furthermore, specific aspects of metacognitive skills were found to be consistently correlated with EEG markers of cognitive effort, regardless of increasing task demands. Finally, behavioral metrics mirroring the efficiency of information processing were found to be associated with different EEG markers of cognitive effort depending on the low or high demand imposed by the task. Full article
(This article belongs to the Special Issue Neuroimaging Techniques in the Measurement of Mental Fatigue)
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