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Related Experiment Video

Updated: Jan 9, 2026

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task
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Towards Objective Cognitive Load Quantification with Multi-modal and Soft Facial Electrophysiology.

Dvir Teitelbaum, Yael Hanein

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Wearable facial electromyography (EMG) offers a new way to measure cognitive load objectively during movement. This technology shows promise for real-time workload monitoring in various applications.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering

    Background:

    • Measuring cognitive load is difficult due to its dynamic nature and reliance on subjective methods.
    • Traditional objective measures like electroencephalography (EEG) are sensitive to artifacts and lab settings.

    Purpose of the Study:

    • To develop and validate a wearable multi-modal system for objective cognitive load assessment.
    • To explore the potential of facial electromyography (EMG) and EEG for real-time monitoring in naturalistic conditions.

    Main Methods:

    • A wearable facial electrode array system recorded EEG and facial EMG signals during a maze navigation and N-back task.
    • Cognitive load was induced, and physiological signals were correlated with subjective load scores.

    Main Results:

    • Subject-specific facial EMG channels strongly correlated with subjective cognitive load, serving as a real-time workload marker.
    • EEG beta entropy decreased over time, indicating cognitive adaptation.
    • Inter-subject variability highlights the need for personalized modeling.

    Conclusions:

    • Wearable facial EMG provides a feasible, non-invasive method for real-time cognitive load assessment.
    • This approach has potential applications in human-computer interaction, neuroergonomics, adaptive learning, and clinical settings for monitoring cognitive fatigue and disorders.