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Cross-task cognitive workload estimation using eye tracking

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Abstract

Cognitive workload is a key factor in understanding human cognitive performance, especially in scenarios that require intensive information processing. This study introduces an innovative method to estimate cognitive workload using eye-tracking data and proposes a novel deep learning model called BiTCADNet (Bidirectional Temporal Convolutional self-Attention Dense Network). Experiments using the newly created dataset "Cognitive-Eye-Movement" and the publicly available dataset "CL-Drive" show that BiTCADNet significantly outperforms traditional deep learning models in terms of accuracy, precision, recall, and F1 scores are significantly better than traditional machine learning methods. The proposed method provides a more effective way to monitor and evaluate cognitive workload in real-time, opening the way for its applications in various human-computer interaction environments.

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Data Availability

All the data contained in this study can be obtained upon request to the corresponding author.

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Funding

This work was supported by the 1. National Key Research and Development Program of China (2022YFC2407006) and 2. Key Research and Development Plan of Shandong Province(2021CXGC011103).

Author information

Authors and Affiliations

  1. Division of Life Science and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Hefei, 230026, China

    Lin Yang & Biao Wang

  2. CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China

    Lin Yang, Lei Wang, Wenchang Xu & Biao Wang

  3. Suzhou ZhongKe Medical Device Industry Development Co, Ltd, Suzhou, 215163, China

    Hanbin Ren

  4. Jinan Guoke Medical Technology Development Co, Ltd, Jinan, 250001, China

    Aijuan Yang

Authors
  1. Lin Yang
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  2. Lei Wang
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  4. Biao Wang
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  5. Hanbin Ren
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Contributions

LinYang was responsible for the conceptualization and methodology of the study, and wrote the main manuscript text. HanbinRen and BiaoWang contributed to the investigation and validation of the research findings. LeiWang, AijuanYang, and WenchangXu were involved in funding acquisition and supervision. LeiWang and WenchangXu were responsible for the review and editing of the main manuscript. All authors reviewed and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Lei Wang.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Yang, L., Wang, L., Xu, W. et al. Cross-task cognitive workload estimation using eye tracking. SIViP 19, 354 (2025). https://doi.org/10.1007/s11760-025-03931-0

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  • DOI: https://doi.org/10.1007/s11760-025-03931-0

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