IMR Press / JIN / Volume 22 / Issue 3 / DOI: 10.31083/j.jin2203062
Open Access Systematic Review
Electroencephalography (EEG) Physiological Indices Reflecting Human Physical Performance: A Systematic Review Using Updated PRISMA
Show Less
1 Department of Industrial Engineering and Management Systems, Arab Academy for Science, Technology, and Maritime Transport, 2913 Alexandria, Egypt
2 Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
3 Department of Psychology, University of Central Florida, Orlando, FL 32816, USA
4 MATériaux et Ingénierie Mécanique (MATIM), Université de Reims Champagne Ardenne, 51100 Reims, France
*Correspondence: (Lina Ismail)
J. Integr. Neurosci. 2023, 22(3), 62;
Submitted: 20 September 2022 | Revised: 14 December 2022 | Accepted: 20 December 2022 | Published: 8 May 2023
Copyright: © 2023 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.

Background: With the advent of portable neurophysiological methods, including electroencephalography, progress in studying brain activity during physical tasks has received considerable attention, predominantly in clinical exercise and sports studies. However, the neural signatures of physical tasks in everyday settings were less addressed. Methods: Electroencephalography (EEG) indices are sensitive to fluctuations in the human brain, reflecting spontaneous brain activity with an excellent temporal resolution. Objective: In this regard, this study attempts to systematically review the feasibility of using EEG indices to quantify human performance in various physical activities in both laboratory and real-world applications. A secondary goal was to examine the feasibility of using EEG indices for quantifying human performance during physical activities with mental tasks. The systematic review was conducted based on the updated Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results: Out of 81 studies, 64 task studies focused on quantifying human performance concerning physical activity, whereas 17 studies focused on quantifying human performance on physical activities associated with mental tasks. EEG studies have primarily relied on linear methods, including the power spectrum, followed by the amplitude of Event-related potential components, to evaluate human physical performance. The nonlinear methods were relatively less addressed in the literature. Most studies focused on assessing the brain activity associated with muscular fatigue tasks. The upper anatomical areas have been discussed in several occupational schemes. The studies addressing biomechanical loading on the torso and spine, which are the risk factors for musculoskeletal disorders, are less addressed. Conclusions: Despite the recent interest in investigating the neural mechanisms underlying human motor functioning, assessing the brain signatures of physical tasks performed in naturalistic settings is still limited.

brain signals
EEG indices
physical activities
physical performance
Fig. 1.
Back to top