International Journal on Science and Technology

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Validating Lightweight Webcam-Based Cognitive Load Estimation Against Neurovascular Gold Standards

Author(s) Punya Taluka
Country India
Abstract Real-time cognitive load estimation has wide applications in education, human-computer interaction, and healthcare, but remains limited by the high cost and intrusiveness of physiological sensors. In this study, we present a feasibility analysis of a lightweight cognitive load classification system based on webcam-accessible behavioral proxies, and benchmark its performance against neurovascular gold standards—namely, electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS). Using a publicly available multimodal dataset collected during a driving task overlaid with an n-back working memory paradigm (0-, 1-, 2-back) , we simulate webcam-based inputs using features such as blink rate, pupil dilation, fixation duration, and saccade metrics.
Supervised learning models were trained to classify cognitive load levels across both multiclass and binary setups. While all models, including those using EEG and fNIRS, performed near chance in the three-class task, a binary classification of high vs. low/moderate load achieved above-chance accuracy using only behavioral features. These results suggest that webcam-derived signals, although coarse, can support lightweight cognitive state monitoring under simplified load distinctions. This study provides a cross-modal validation of scalable, low-cost cognitive load assessment approaches and highlights important trade-offs between accessibility, resolution, and physiological fidelity.
Keywords Cognitive load, EEG, fNIRS, webcam estimation
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-06-05
DOI https://doi.org/10.71097/IJSAT.v16.i2.5949
Short DOI https://doi.org/g9pz7f
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