Paraphernalia
PPubMed25 Apr 2025Cited 19×

Systematic review of neurophysiological assessment techniques and metrics for mental workload evaluation in real-world settings

Moussa Diarra, Jean Theurel, Benjamin Paty

Abstract

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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Systematic review of neurophysiological assessment techniques and metrics for mental workload evaluation in real-world settings · Paraphernalia