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    Area of Science:

    • Neuroscience
    • Cognitive Science
    • Biomedical Engineering

    Background:

    • Cognitive workload, the mental effort in tasks, can be measured using neurophysiological signals like electroencephalogram (EEG).
    • Brain-Computer Interfaces (BCIs) offer a potential avenue for real-time cognitive workload assessment.

    Purpose of the Study:

    • To evaluate the efficacy of an EEG-based BCI in discriminating varying cognitive workload levels.
    • To assess the BCI's performance in classifying difficulty levels of a mental arithmetic task.

    Main Methods:

    • Collected EEG data from 10 subjects performing mental addition at easy, medium, and hard difficulty levels.
    • Extracted EEG features using band power and Common Spatial Pattern (CSP).
    • Employed Fisher Ratio for feature selection and a Linear Discriminant Classifier (LDC) for classification.

    Main Results:

    • Achieved an average accuracy of 90% for discriminating between easy and hard tasks (2 classes).
    • Obtained an average accuracy of 66% for discriminating between easy, medium, and hard tasks (3 classes).

    Conclusions:

    • Demonstrated the feasibility of using EEG-based BCIs for measuring cognitive workload.
    • Indicated potential for BCI applications in monitoring mental effort during cognitive tasks.