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Measuring Cognitive Load: Leveraging fNIRS and Machine Learning for Classification of Workload Levels

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1963))

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Abstract

Measuring cognitive load, a subjective construct that reflects the mental effort required for a given task, remains a challenging endeavor. While Functional Near-Infrared Spectroscopy (fNIRS) has been utilized in the field of neurology to assess cognitive load, there are limited studies that have specifically focused on high cognitive load scenarios. Previous research in the field of cognitive workload assessment using fNIRS has primarily focused on differentiating between two levels of mental workload. These studies have explored the classification of low and high levels of cognitive load, or easy and difficult tasks, using various Machine Learning (ML) and Deep Learning (DL) models. However, there is a need to further investigate the detection of multiple levels of cognitive load to provide more fine-grained information about the mental state of an individual. This study aims to classify four mental workload levels using classical ML techniques, specifically random forests, with fNIRS data. It assesses the effectiveness of ML algorithms with fNIRS data, provides insights into classification features and patterns, and contributes to understanding neural mechanisms in cognitive processing. ML algorithms used for classification include Naïve Bayes, k-Nearest Neighbors (k-NN), Decision Trees, Random Forests, and Nearest Centroid. Random Forests achieved a promising accuracy and Area Under Curve (AUC) of around 99.99%. The findings of this study highlight the potential of utilizing fNIRS and ML algorithms for accurately classifying cognitive workload levels. The use of multiple features extracted from fNIRS data may contribute to a more robust and reliable classification approach.

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Author information

Authors and Affiliations

  1. Institute for Intelligent Systems Research and Innovation Geelong, Deakin University, Victoria, Australia

    Mehshan Ahmed Khan, Houshyar Asadi, Chee Peng Lim & Saeid Nahavandi

  2. Faculty of Sci Eng and Built Env, School of Info Technology, Deakin University, Geelong, VIC, Australia

    Thuong Hoang

Authors
  1. Mehshan Ahmed Khan
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  2. Houshyar Asadi
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  3. Thuong Hoang
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  4. Chee Peng Lim
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  5. Saeid Nahavandi
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Corresponding author

Correspondence to Mehshan Ahmed Khan .

Editor information

Editors and Affiliations

  1. School of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Khan, M.A., Asadi, H., Hoang, T., Lim, C.P., Nahavandi, S. (2024). Measuring Cognitive Load: Leveraging fNIRS and Machine Learning for Classification of Workload Levels. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1963. Springer, Singapore. https://doi.org/10.1007/978-981-99-8138-0_25

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  • DOI: https://doi.org/10.1007/978-981-99-8138-0_25

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8137-3

  • Online ISBN: 978-981-99-8138-0

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