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Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
Published on: June 15, 2018
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Effects of Mental Workload Manipulation on Electroencephalography Spectrum Oscillation and Microstates in
Wenbin Li1, Shan Cheng2, Jing Dai3
1Department of Aerospace Hygiene, Faculty of Aerospace Medicine, Air Force Medical University, Xi'an, China.
Brain and Behavior
|January 8, 2025
Summary
High mental workload during flight simulation increases electroencephalography (EEG) theta, alpha, and beta band powers. EEG microstate and frequency band analyses effectively detect elevated mental workload and brain network changes.
Area of Science:
- Neuroscience
- Cognitive Psychology
- Human Factors Engineering
Background:
- High mental workload during flight operations impairs task performance.
- Electroencephalography (EEG) offers methods like power spectral analysis and microstate analysis to assess cognitive load.
- This study investigates EEG responses to simulated flight multitasking under varying mental workloads.
Purpose of the Study:
- To explore the impact of high mental workload on EEG frequency-band power and microstate parameters during simulated flight multitasking.
- To determine if EEG measures can effectively differentiate between low and high mental workload conditions.
Main Methods:
- Thirty-six participants underwent simulated flight multitasking under low and high mental workload conditions.
- EEG signals were recorded and analyzed using power spectral analysis (delta, theta, alpha, beta bands) and microstate analysis (classes A-D).
- Comparisons were made between workload conditions, and correlations between EEG indices were examined.
Main Results:
- Elevated theta, alpha, and beta band powers were observed under high mental workload.
- High workload was associated with reduced global explained variance and altered microstate parameters (decreased B, increased D) and transition frequencies.
- Significant correlations were found between specific microstate parameters and frequency band powers.
Conclusions:
- EEG frequency-band power and microstate parameters serve as reliable indicators for detecting high mental workload.
- Power spectral and microstate analyses are interconnected and provide complementary insights into brain network dynamics during cognitive tasks.
