Neuroimaging Techniques in the Measurement of Mental Fatigue

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

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