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: closed (30 November 2024) | Viewed by 10970
Special Issue Editors
Interests: cognitive neuroscience; machine learning; neuroscience; signal processing; human factors
Special Issues, Collections and Topics in MDPI journals
Interests: cognitive neuroscience; neuroscience; signal processing; human factors
Special Issues, Collections and Topics in MDPI journals
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.
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Keywords
- mental states
- mental fatigue (MF)
- Artificial Intelligence (AI)
- multivariate autoregressive (MVAR)
- multimodal approach
- neuroimaging
- neuroscience
- decision support system (DSS)
- neurophysiological
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