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Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
Published on: June 15, 2018
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Evidence for modulation of EEG microstates by mental workload levels and task types
Jingxin Chen1,2, Yufeng Ke1,2, Guangjian Ni1,2
1Academy of Medical Engineering and Translational Medicine, Tianjin International Joint Research Centre for Neural Engineering, and Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, People's Republic of China.
Human Brain Mapping
|December 5, 2023
Summary
Electroencephalography (EEG) microstate analysis reveals that mental workload (MWL) impacts brain activity differently across tasks. Microstate parameters can distinguish MWL levels, but task-dependency is key.
Area of Science:
- Neuroscience
- Cognitive Science
- Electrophysiology
Background:
- Electroencephalography (EEG) microstate analysis investigates brain's large-scale electrical activity dynamics.
- Canonical microstates (A, B, C, D) and their parameters are linked to cognitive functions and disorders.
- Previous studies suggest EEG microstates are modulated by mental workload (MWL), but task-specificity remains unclear.
Purpose of the Study:
- To investigate if EEG microstate modulation by MWL is consistent across different tasks.
- To compare microstate topographies and dynamics under MWL in the NBack and Multi-Attribute Task Battery (MATB) tasks.
- To assess the potential of microstate parameters for classifying MWL levels.
Main Methods:
- EEG data were collected during NBack and MATB tasks under varying MWL conditions.
- Microstate analysis identified canonical topographies and parameters.
- Support Vector Machine Recursive Feature Elimination (SVM-RFE) was used for feature selection and MWL classification.
Main Results:
- MWL modulation of microstate topographies and parameters differed significantly between NBack and MATB tasks.
- NBack task showed significant differences in all microstate topographies and parameters A and C under MWL.
- MATB task did not show significant differences in microstate parameters under MWL.
- Machine learning models achieved high accuracy (87% within-task, 78% cross-task) in discriminating MWL levels using microstate features.
Conclusions:
- EEG microstate analysis provides insights into neural activity patterns related to MWL.
- The impact of MWL on microstates is task-dependent, reflecting distinct functional systems.
- Microstate parameters show promise as objective indicators for distinguishing MWL levels.
