Department of Industrial Engineering and Management Systems, Computational Neuroergonomics Laboratory, University of Central Florida, Orlando, FL, United States of America.
PLoS One. 2020 Dec 4;15(12):e0242857. doi: 10.1371/journal.pone.0242857. eCollection 2020.
Neuroergonomics combines neuroscience with ergonomics to study human performance using recorded brain signals. Such neural signatures of performance can be measured using a variety of neuroimaging techniques, including functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), and electroencephalography (EEG). EEG has an excellent temporal resolution, and EEG indices are highly sensitive to human brain activity fluctuations.
The focus of this systematic review was to explore the applications of EEG indices for quantifying human performance in a variety of cognitive tasks at the macro and micro scales. To identify trends and the state of the field, we examined global patterns among selected articles, such as journal contributions, highly cited papers, affiliations, and high-frequency keywords. Moreover, we discussed the most frequently used EEG indices and synthesized current knowledge regarding the EEG signatures of associated human performance measurements.
In this systematic review, we analyzed articles published in English (from peer-reviewed journals, proceedings, and conference papers), Ph.D. dissertations, textbooks, and reference books. All articles reviewed herein included exclusively EEG-based experimental studies in healthy participants. We searched Web-of-Science and Scopus databases using specific sets of keywords.
Out of 143 papers, a considerable number of cognitive studies focused on quantifying human performance with respect to mental fatigue, mental workload, mental effort, visual fatigue, emotion, and stress. An increasing trend for publication in this area was observed, with the highest number of publications in 2017. Most studies applied linear methods (e.g., EEG power spectral density and the amplitude of event-related potentials) to evaluate human cognitive performance. A few papers utilized nonlinear methods, such as fractal dimension, largest Lyapunov exponent, and signal entropy. More than 50% of the studies focused on evaluating an individual's mental states while operating a vehicle. Several different methods of artifact removal have also been noted. Based on the reviewed articles, research gaps, trends, and potential directions for future research were explored.
This systematic review synthesized current knowledge regarding the application of EEG indices for quantifying human performance in a wide variety of cognitive tasks. This knowledge is useful for understanding the global patterns of applications of EEG indices for the analysis and design of cognitive tasks.
神经工效学将神经科学与工效学相结合，通过记录大脑信号来研究人类表现。可以使用各种神经影像学技术来测量这种表现的神经特征，包括功能磁共振成像（fMRI）、功能性近红外光谱（fNIRS）和脑电图（EEG）。EEG 具有出色的时间分辨率，并且 EEG 指标对人脑活动波动非常敏感。
本系统评价的重点是探索 EEG 指标在宏观和微观尺度上量化各种认知任务中人类表现的应用。为了确定趋势和领域状态，我们检查了选定文章中的全局模式，例如期刊贡献、高引论文、附属机构和高频关键词。此外，我们讨论了最常用的 EEG 指标，并综合了与相关人类表现测量相关的 EEG 特征的当前知识。
在本系统评价中，我们分析了发表在英语（同行评议期刊、会议录和会议论文）、博士论文、教科书和参考书的文章。本文综述中所有的文章都仅包含基于 EEG 的健康参与者的实验研究。我们使用特定的关键词集在 Web-of-Science 和 Scopus 数据库中进行搜索。
在 143 篇论文中，相当多的认知研究侧重于量化与精神疲劳、精神工作负荷、精神努力、视觉疲劳、情绪和压力相关的人类表现。观察到该领域的出版呈增长趋势，2017 年发表的论文数量最多。大多数研究应用线性方法（例如 EEG 功率谱密度和事件相关电位的振幅）来评估人类认知表现。少数论文使用了非线性方法，例如分形维数、最大李雅普诺夫指数和信号熵。超过 50%的研究侧重于评估个体在驾驶时的精神状态。还注意到了几种不同的去除伪影的方法。根据综述文章，探讨了研究差距、趋势和未来研究的潜在方向。
本系统评价综合了当前关于 EEG 指标在各种认知任务中量化人类表现的应用知识。这些知识有助于理解 EEG 指标在认知任务分析和设计中的应用的全局模式。
