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Updated: May 14, 2025

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
Published on: July 1, 2015
Systematic review of neurophysiological assessment techniques and metrics for mental workload evaluation in
Moussa Diarra1, Jean Theurel1, Benjamin Paty1
1National Research and Safety Institute for the Prevention of Occupational Accidents and Diseases (INRS) - Applied Sciences for Work and Organisations Department, Vandoeuvre-les-Nancy, France.
Introduction:
Mental Workload (MWL) is a concept that has garnered increasing interest in professional settings but remains challenging to define consensually. The literature reports a plurality of operational definitions and assessment methods, with no established unified framework. This review aims to identify objective and validated measurement methods for evaluating MWL in real-world work contexts. Particular attention is given to neurophysiological methods, recognized for their efficiency and robustness, enabling real-time assessment without disrupting operator activity.
Method:
To conduct this analysis, a systematic search was performed in three databases (PubMed, ScienceDirect, and IEEEXplore), covering studies published from their inception until March 30, 2023. Selection criteria included research focusing on MWL and its derivatives, as well as neurophysiological measures applied in real-world conditions. An initial screening based on titles and abstracts was followed by an in-depth review, assisted by the bibliometric software Rayyan.
Results:
The explored concepts, applied methods, and study results were compiled into a synthesis table. Ultimately, 35 studies were included, highlighting the diversity of measurement tools used in field settings, often combined with subjective assessments.
Discussion:
Furthermore, key physiological indicators such as ECG, eye data, EEG and the relationship between MWL metrics and those uses to measure stress are emphasized and discussed. A better understanding of these interrelations could refine the assessment of their respective impacts and help anticipate their consequences on workers' mental health and safety.
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