Skip to main content
SpringerLink
Log in

Wavelet-Based Analysis of fNIRS Measures Enable Assessment of Workload

  • Conference paper
  • First Online:
Augmented Cognition (HCII 2022)

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

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Paas, F., Tuovinen, J.E., Tabbers, H., Van Gerven, P.W.M.: Cognitive load measurement as a means to advance cognitive load theory. Educ. Psychol. 38, 63–71 (2003). https://doi.org/10.1207/S15326985EP3801_8

    Article  Google Scholar 

  2. Curtin, A., Ayaz, H.: The age of neuroergonomics: towards ubiquitous and continuous measurement of brain function with fNIRS. Jpn. Psychol. Res. 60, 374–386 (2018). https://doi.org/10.1111/jpr.12227

    Article  Google Scholar 

  3. Pinti, P., Scholkmann, F., Hamilton, A., et al.: Current status and issues regarding pre-processing of fNIRS neuroimaging data: an investigation of diverse signal filtering methods within a general linear model framework. Front. Hum. Neurosci. 12, 505 (2019). https://doi.org/10.3389/fnhum.2018.00505

    Article  Google Scholar 

  4. Reddy, P., Izzetoglu, M., Shewokis, P.A., et al.: Evaluation of fNIRS signal components elicited by cognitive and hypercapnic stimuli. Sci. Rep. 111(11), 1–15 (2021). https://doi.org/10.1038/s41598-021-02076-7

    Article  Google Scholar 

  5. Hakimi, N., Jodeiri, A., Mirbagheri, M., Kamaledin Setarehdan, S.: Proposing a convolutional neural network for stress assessment by means of derived heart rate from functional near infrared spectroscopy. Comput. Biol. Med. 121, 103810 (2020). https://doi.org/10.1016/j.compbiomed.2020.103810

  6. Charles, R.L., Nixon, J.: Measuring mental workload using physiological measures: a systematic review. Appl. Ergon. 74, 221–232 (2019). https://doi.org/10.1016/J.APERGO.2018.08.028

    Article  Google Scholar 

  7. Stefanovska, A., Bracic, M., Kvernmo, H.: Wavelet analysis of oscillations in the peripheral blood circulation measured by laser doppler technique. IEEE Trans. Biomed. Eng. 46, 1230–1239 (1999). https://doi.org/10.1109/10.790500

    Article  Google Scholar 

  8. Kvandal, P., Landsverk, S.A., Bernjak, A., et al.: Low-frequency oscillations of the laser Doppler perfusion signal in human skin. Microvasc. Res. 72, 120–127 (2006). https://doi.org/10.1016/J.MVR.2006.05.006

    Article  Google Scholar 

  9. Molavi, B., Dumont, G.A.: Wavelet-based motion artifact removal for functional near-infrared spectroscopy. Physiol. Meas. 33, 259–270 (2012). https://doi.org/10.1088/0967-3334/33/2/259

    Article  Google Scholar 

  10. Holper, L., Scholkmann, F., Seifritz, E.: Prefrontal hemodynamic after-effects caused by rebreathing may predict affective states – a multimodal functional near-infrared spectroscopy study. Brain Imaging Behav. 11(2), 461–472 (2016). https://doi.org/10.1007/s11682-016-9527-4

    Article  Google Scholar 

  11. Highton, D., Ghosh, A., Tachtsidis, I., et al.: Monitoring cerebral autoregulation after brain injury: multimodal assessment of cerebral slow-wave oscillations using near-infrared spectroscopy. Anesth. Analg. 121, 198 (2015). https://doi.org/10.1213/ANE.0000000000000790

    Article  Google Scholar 

  12. Xu, J., Slagle, J.M., Banerjee, A., et al.: Use of a portable functional near-infrared spectroscopy (fNIRS) system to examine team experience during crisis event management in clinical simulations. Front. Hum. Neurosci. 13, 85 (2019). https://doi.org/10.3389/FNHUM.2019.00085/BIBTEX

    Article  Google Scholar 

  13. Wang, F., Jiang, Z., Li, X., et al.: Functional brain network analysis of knowledge transfer while engineering problem-solving. Front. Hum. Neurosci. 15 (2021). https://doi.org/10.3389/FNHUM.2021.713692

  14. Xu, L., Wang, B., Xu, G., et al.: Functional connectivity analysis using fNIRS in healthy subjects during prolonged simulated driving. Neurosci. Lett. 640, 21–28 (2017). https://doi.org/10.1016/J.NEULET.2017.01.018

    Article  Google Scholar 

  15. Zhang, L., Sun, J., Sun, B., et al.: Studying hemispheric lateralization during a Stroop task through near-infrared spectroscopy-based connectivity. 19, 057012 (2014) . https://doi.org/10.1117/1.JBO.19.5.057012

  16. Verdière, K.J., Roy, R.N., Dehais, F.: Detecting pilot’s engagement using fnirs connectivity features in an automated vs. Manual landing scenario. Front. Hum. Neurosci. 12, 6 (2018). https://doi.org/10.3389/FNHUM.2018.00006/BIBTEX

    Article  Google Scholar 

  17. Reddy, P., Kerr, J., Shewokis, P.A., Izzetoglu, K.: Brain activity changes elicited through multi-session training assessment in the prefrontal cortex by fNIRS. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) HCII 2021. LNCS (LNAI), vol. 12776, pp. 63–73. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-78114-9_5

    Chapter  Google Scholar 

  18. Ayaz, H., Shewokis, P.A., Curtin, A., et al.: Using MazeSuite and functional near infrared spectroscopy to study learning in spatial navigation. J. Vis. Exp. 8, 3443 (2011). https://doi.org/10.3791/3443

    Article  Google Scholar 

  19. Izzetoglu, M., Izzetoglu, K.: Real time artifact removal. 1–9 (2014)

    Google Scholar 

  20. Villringer, A., Chance, B.: Non invasive optical spectroscopy and imaging of human brain function. Trends Neurosci. 20, 435–442 (1997). https://doi.org/10.1016/S0166-2236(97)01132-6

    Article  Google Scholar 

  21. Scholkmann, F., Wolf, M.: General equation for the differential pathlength factor of the frontal human head depending on wavelength and age. J. Biomed. Opt. 18, 105004 (2013). https://doi.org/10.1117/1.jbo.18.10.105004

    Article  Google Scholar 

  22. Scholkmann, F., Spichtig, S., Muehlemann, T., Wolf, M.: How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation. Physiol. Meas. 31, 649–662 (2010). https://doi.org/10.1088/0967-3334/31/5/004

    Article  Google Scholar 

  23. Iatsenko, D., McClintock, P.V.E., Stefanovska, A.: Linear and synchrosqueezed time-frequency representations revisited: overview, standards of use, resolution, reconstruction, concentration, and algorithms. Digit. Signal Process. A Rev. J. 42, 1–26 (2015). https://doi.org/10.1016/j.dsp.2015.03.004

    Article  MathSciNet  Google Scholar 

  24. Kirilina, E., Yu, N., Jelzow, A., et al.: Identifying and quantifying main components of physiological noise in functional near infrared spectroscopy on the prefrontal cortex. Front. Hum. Neurosci. 7, 864 (2013). https://doi.org/10.3389/fnhum.2013.00864

  25. Yücel, M.A., Selb, J., Aasted, C.M., et al.: Mayer waves reduce the accuracy of estimated hemodynamic response functions in functional near-infrared spectroscopy. Biomed. Opt. Express 7, 3078–3088 (2016). https://doi.org/10.1364/boe.7.003078

    Article  Google Scholar 

  26. Bates, D., Mächler, M., Bolker, B.M., Walker, S.C.: Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015). https://doi.org/10.18637/jss.v067.i01

  27. Kuznetsova, A., Brockhoff, P.B., Christensen, R.H.B.: lmerTest package: tests in linear mixed effects models lmertest package: tests in linear mixed effects models. J. Stat. Softw. 82 (2017). https://doi.org/10.18637/jss.v082.i13

  28. length, R.: emmeans: Estimated Marginal Means, aka LeastSquares Means (2020)

    Google Scholar 

  29. Friston, K.J.: Statistical Parametric Mapping. In: Kötter, R. (eds.) Neuroscience Databases, pp. 237–250. Springer, Boston (2003). https://doi.org/10.1007/978-1-4615-1079-6_16

  30. Westfall, J., Kenny, D.A., Judd, C.M.: Statistical power and optimal design in experiments in which samples of participants respond to samples of stimuli. J. Exp. Psychol. Gen. 143, 2020–2045 (2014). https://doi.org/10.1037/xge0000014

    Article  Google Scholar 

  31. Izzetoglu, K., Aksoy, M.E., Agrali, A., et al.: Studying brain activation during skill acquisition via robot-assisted surgery training. Brain Sci. 11, 937 (2021). https://doi.org/10.3390/BRAINSCI11070937

  32. Seghier, M.L., Price, C.J.: Interpreting and utilising intersubject variability in brain function. Trends Cogn. Sci. 22, 517–530 (2018). https://doi.org/10.1016/j.tics.2018.03.003

    Article  Google Scholar 

  33. Izzetoglu, K., Ayaz, H., Hing, J.T., et al.: UAV operators workload assessment by optical brain imaging technology (fNIR). In: Valavanis, K., Vachtsevanos, G. (eds.) Handbook of Unmanned Aerial Vehicles, pp. 2475–2500. Springer, Dordrecht (2015). https://doi.org/10.1007/978-90-481-9707-1_22

  34. Izzetoglu, M., Bunce, S.C., Izzetoglu, K., et al.: Functional brain imaging using near-infrared technology. IEEE Eng. Med. Biol. Mag. 26, 38–46 (2007)

    Article  Google Scholar 

  35. Shewokis, P.A., Shariff, F.U., Liu, Y., et al.: Acquisition, retention and transfer of simulated laparoscopic tasks using fNIR and a contextual interference paradigm. Am. J. Surg. 213, 336–345 (2017). https://doi.org/10.1016/j.amjsurg.2016.11.043

    Article  Google Scholar 

  36. Mandrick, K., Peysakhovich, V., Rémy, F., et al.: Neural and psychophysiological correlates of human performance under stress and high mental workload. Biol. Psychol. 121, 62–73 (2016). https://doi.org/10.1016/j.biopsycho.2016.10.002

    Article  Google Scholar 

  37. Liu, Y., Ayaz, H., Shewokis, P.A.: Multisubject “learning” for mental workload classification using concurrent EEG, fNIRS, and physiological measures. Front. Hum. Neurosci. 11, 389 (2017). https://doi.org/10.3389/fnhum.2017.00389

    Article  Google Scholar 

  38. Palma Fraga, R., Reddy, P., Kang, Z., Izzetoglu, K.: Multimodal analysis using neuroimaging and eye movements to assess cognitive workload. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) HCII 2020. LNCS (LNAI), vol. 12196, pp. 50–63. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50353-6_4

    Chapter  Google Scholar 

Download references

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kurtulus Izzetoglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Patricia A. Shewokis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Patricia A. Shewokis .

Editor information

Editors and Affiliations

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

    Dylan D. Schmorrow

  2. Katmai Government Services, Orlando, FL, USA

    Cali M. Fidopiastis

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05456-3

  • Online ISBN: 978-3-031-05457-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation