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A Database for Cognitive Workload Classification Using Electrocardiogram and Respiration Signal

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Advances in Neuroergonomics and Cognitive Engineering (AHFE 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 259))

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Abstract

Cognitive workload is a critical factor in determining the level of attentional effort exerted by users. Understanding and classifying cognitive workload is challenging as individuals exert varying levels of mental effort to meet the task's underlying demands. Twenty-six participants (12M, 14F, Mean = 22.68 ± 5.10) were exposed to two different tasks designed to induce low and high cognitive workloads. Subjective and objective measures were collected to create a novel, validated multimodal dataset for cognitive workload classification. Participants’ perceived workload was collected using the NASA-TLX. Electrocardiogram (ECG) and Respiration (RR) data were collected to extract the Heart Rate Variability and Respiration Rate Variability features. Four machine learning algorithms were utilized to classify cognitive workload levels where AdaBoost classifier achieved the highest Leave-One-Subject-Out Cross-Validation accuracy, and F1-Score of 80.2%, 80.3% respectively. This is the first publicly available dataset with ECG, RR and subjective responses for cognitive workload classification.

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Notes

  1. 1.

    https://gitlab.com/hilabmsu/cw-database.

  2. 2.

    https://www.biopac.com/.

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Acknowledgments

We thank all the members of the HI Lab for helping with the data collection and or the valuable comments raised from discussions with them. This research was supported by the National Science Foundation under award number 1900704. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Science Foundation.

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Authors and Affiliations

  1. Gianforte School of Computing, Montana State University, Bozeman, USA

    Apostolos Kalatzis, Ashish Teotia & Laura Stanley

  2. Department of Industrial Engineering, Clemson University, Clemson, USA

    Vishnunarayan Girishan Prabhu

Authors
  1. Apostolos Kalatzis
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  2. Ashish Teotia
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  3. Vishnunarayan Girishan Prabhu
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  4. Laura Stanley
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Corresponding author

Correspondence to Apostolos Kalatzis .

Editor information

Editors and Affiliations

  1. School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, USA

    Hasan Ayaz

  2. School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Umer Asgher

  3. DIGITAL – Institute for Information and Communication Technologies, Graz, Austria

    Lucas Paletta

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Kalatzis, A., Teotia, A., Prabhu, V.G., Stanley, L. (2021). A Database for Cognitive Workload Classification Using Electrocardiogram and Respiration Signal. In: Ayaz, H., Asgher, U., Paletta, L. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2021. Lecture Notes in Networks and Systems, vol 259. Springer, Cham. https://doi.org/10.1007/978-3-030-80285-1_58

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  • DOI: https://doi.org/10.1007/978-3-030-80285-1_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-80284-4

  • Online ISBN: 978-3-030-80285-1

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