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    This study presents an open-access electroencephalography dataset for multitasking mental workload. The data achieved 69% classification accuracy for workload levels, aiding operator performance analysis and Brain-Computer Interface (BCI) development.

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    Area of Science:

    • Neuroscience
    • Cognitive Science
    • Human-Computer Interaction

    Background:

    • Multitasking mental workload assessment is crucial for operator performance and BCI development.
    • Existing datasets may lack comprehensive multitasking workload data.
    • Standardized EEG data is needed for reproducible research.

    Purpose of the Study:

    • To introduce a novel, open-access electroencephalography (EEG) dataset for multitasking mental workload.
    • To validate the dataset using established EEG analysis tools and statistical methods.
    • To demonstrate the utility of the dataset for workload classification.

    Main Methods:

    • Collected EEG data from 48 subjects during a simultaneous capacity (SIMKAP) multitasking experiment.
    • Utilized EEGLAB for EEG spectral activity analysis to identify significant channels and features.
    • Employed Neighborhood Component Analysis (NCA) and Support Vector Regression (SVR) for workload classification.

    Main Results:

    • EEG spectral activity analysis highlighted significant channels and activities relevant to mental workload.
    • A classification accuracy of 69% was achieved for three distinct workload levels.
    • Cohen's kappa coefficient of 0.46 indicated moderate agreement in workload discrimination.

    Conclusions:

    • The presented EEG dataset provides a valuable resource for studying multitasking mental workload.
    • The findings demonstrate the feasibility of accurately classifying mental workload levels using EEG data.
    • This work contributes to advancing operator performance analysis and Brain-Computer Interface (BCI) research.