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Wavelet-Based Analysis of fNIRS Measures Enable Assessment of Workload

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Augmented Cognition (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13310))

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Abstract

Functional near infrared spectroscopy (fNIRS) measurements are confounded by signals originating from different physiological causes (i.e., neuronal, Mayer wave, respiratory, and cardiac), whose time-frequency characteristics are modulated by the experimental task. Most fNIRS research reports workload measures from very low frequency (VLF) band as it correlates to neuronal activity and considers systemic factors (i.e., Mayer wave, respiratory, and cardiac) as noise. However, studies using the physiological sensors have extensively shown that inclusion of systemic factors improve assessment of workload. Wavelet analysis enables investigation of physiological factors of varying temporal and frequency characteristics within the same plane. Therefore, this study aims to investigates task-evoked effects on the fNIRS measurements originating from different physiological sources using wavelet-based analysis. To accomplish this objective, we used the data collected from 13 novice participants who underwent a realistic training protocol that consisted of two easy sessions and one hard session. We extracted time-averaged wavelet-features (relative energy density and relative amplitude) from different physiological bands (cardiac, respiratory, Mayer wave, and neuronal) and hemispheres (right and left). Firstly, results indicated that wavelet-features increased across sessions within VLF bands and decreased within cardiac bands. No changes were observed in Mayer wave and respiratory bands. Secondly, interaction between task load and hemisphere was only observed in VLF band. In conclusion, these results indicate that wavelet-based analysis of fNIRS signals is not only sensitive in detecting workload changes but can also provide complimentary information regarding physiological changes.

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Authors and Affiliations

  1. School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, 19104, USA

    Pratusha Reddy, Kurtulus Izzetoglu & Patricia A. Shewokis

  2. Nutrition Science Department, College of Nursing and Health Professions, Drexel University, Philadelphia, PA, 19104, USA

    Patricia A. Shewokis

Authors
  1. Pratusha Reddy
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  2. Kurtulus Izzetoglu
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  3. Patricia A. Shewokis
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Correspondence to Patricia A. Shewokis .

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Editors and Affiliations

  1. Soar Technology Inc., Orlando, FL, USA

    Dylan D. Schmorrow

  2. Katmai Government Services, Orlando, FL, USA

    Cali M. Fidopiastis

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Reddy, P., Izzetoglu, K., Shewokis, P.A. (2022). Wavelet-Based Analysis of fNIRS Measures Enable Assessment of Workload. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Augmented Cognition. HCII 2022. Lecture Notes in Computer Science(), vol 13310. Springer, Cham. https://doi.org/10.1007/978-3-031-05457-0_15

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  • DOI: https://doi.org/10.1007/978-3-031-05457-0_15

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