Electroencephalography (EEG) Physiological Indices Reflecting Human Physical Performance: A Systematic Review Using Updated PRISMA.

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.

Modeling Brain Functional Connectivity Patterns during an Isometric Arm Force Exertion Task at Different Levels of Perceived Exertion: A Graph Theoretical Approach.

The perception of physical exertion is the cognitive sensation of work demands associated with voluntary muscular actions. Measurements of exerted force are crucial for avoiding the risk of overexertion and understanding human physical capability. For this purpose, various physiological measures have been used; however, the state-of-the-art in-force exertion evaluation lacks assessments of underlying neurophysiological signals. The current study applied a graph theoretical approach to investigate the topological changes in the functional brain network induced by predefined force exertion levels for twelve female participants during an isometric arm task and rated their perceived physical comfort levels. The functional connectivity under predefined force exertion levels was assessed using the coherence method for 84 anatomical brain regions of interest at the electroencephalogram (EEG) source level. Then, graph measures were calculated to quantify the network topology for two frequency bands. The results showed that high-level force exertions are associated with brain networks characterized by more significant clustering coefficients (6%), greater modularity (5%), higher global efficiency (9%), and less distance synchronization (25%) under alpha coherence. This study on the neurophysiological basis of physical exertions with various force levels suggests that brain regions communicate and cooperate higher when muscle force exertions increase to meet the demands of physically challenging tasks.