Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis

Ismail, Lina Elsherif; Karwowski, Waldemar · 2020 · Crossref

DOI: 10.1371/journal.pone.0242857

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Summary

This systematic review and bibliometric analysis addresses the application of electroencephalography (EEG) indices for quantifying human cognitive performance within the field of neuroergonomics. Motivated by the limitations of traditional subjective methods in assessing complex human-technology interactions, the study aims to synthesize current knowledge regarding EEG signatures of performance. The authors sought to identify global trends, dominant EEG indices, and research gaps by analyzing literature at both macro and micro scales, focusing on healthy participants engaged in cognitive tasks with minimal physical requirements. The researchers conducted a comprehensive search of Web-of-Science and Scopus databases for articles published between 2000 and 2019, adhering to PRISMA guidelines. Initial searches yielded 1,767 articles, which were refined through keyword filtering and manual screening of titles, abstracts, and full texts. The final dataset comprised 143 eligible studies exclusively using EEG, excluding those involving clinical populations or combined neuroimaging techniques. Data extraction focused on physiological measurements, EEG indices, cognitive tasks, and methods for artifact removal and feature classification. Bibliometric tools, including VOSviewer, were employed to map co-citations, keyword co-occurrence, and source relationships. The analysis revealed a significant increasing trend in publications, peaking in 2017, with dominant contributions from the United States, China, Germany, and France. Most studies utilized linear methods, particularly power spectral density (PSD) and event-related potentials (ERPs), while fewer employed nonlinear methods like entropy or fractal dimension. Over 50% of the reviewed studies focused on vehicle operation, specifically monitoring mental fatigue, drowsiness, and alertness to enhance driving safety. Key findings indicated that PSD and ERPs (such as P300) are the most frequently used indices for assessing mental workload, attention, and working memory. Independent component analysis (ICA) was the predominant method for artifact removal, and machine learning algorithms, particularly support vector machines, were widely used for classifying mental states. The study concludes that EEG indices provide reliable, objective measures for quantifying cognitive performance in real-world applications, particularly in high-stakes environments like driving. The synthesis highlights the field's shift toward adaptive systems and automated monitoring to mitigate human error. By identifying dominant methodologies and frequent applications, the review offers a structured overview of the state of neuroergonomics, guiding future research toward addressing existing gaps in predicting and modeling operator functional states.

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Topics

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• neuro workload indices
• workload measurement
• drowsiness detection algorithms

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• Empirical Findings: physiological data