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The Potential of Functional Near-Infrared Spectroscopy (fNIRS) for Motion-Intensive Game Paradigms

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Games and Learning Alliance (GALA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13134))

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Abstract

Functional near-infrared spectroscopy (fNIRS) is gaining popularity as a non-invasive neuroimaging technique in a broad range of fields, including the context of gaming and serious games. However, the capabilities of fNIRS are still underutilized. FNIRS is less prone to motion artifacts and more portable in comparison to other neuroimaging methods and it is therefore ideal for experimental designs which involve physical activity. In this paper, the goal is to demonstrate the feasibility of fNIRS for the recording of cortical activation during a motion-intensive task, namely basketball dribbling. FNIRS recordings over sensorimotor regions were conducted in a block-design on 20 participants, who dribbled a basketball with their dominant right hand. Signal quality for task-related concentration changes in oxy-Hb and deoxy-Hb has been investigated by means of the contrast-to-noise ratio (CNR). A statistical comparison of average CNR from the fNIRS signal revealed the expected effect of significantly higher CNR over the left as compared to the right sensorimotor region. Our findings demonstrate that fNIRS delivers sufficient signal quality to measure hemispheric activation differences during a motion-intensive motoric task like basketball dribbling and bare indications for future endeavors with fNIRS in less constraint settings.

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References

  1. Kober, S.E., Wood, G., Kiili, K., Moeller, K., Ninaus, M.: Game-based learning environments affect frontal brain activity. PLoS One 15, e0242573 (2020). https://doi.org/10.1371/journal.pone.0242573

    Article  Google Scholar 

  2. Wang, Z., et al.: Effects of three different rehabilitation games’ interaction on brain activation using functional near-infrared spectroscopy. Physiol. Meas. 41, 125005 (2020). https://doi.org/10.1088/1361-6579/abcd1f

    Article  Google Scholar 

  3. Ninaus, M., et al.: Neurophysiological methods for monitoring brain activity in serious games and virtual environments: a review. Int. J. Technol. Enhanced Learn. 6, 78–103 (2014). https://doi.org/10.1504/IJTEL.2014.060022

    Article  Google Scholar 

  4. Witte, M., Ninaus, M., Kober, S.E., Neuper, C., Wood, G.: Neuronal correlates of cognitive control during gaming revealed by near-infrared spectroscopy. PLoS One 10, e0134816 (2015). https://doi.org/10.1371/journal.pone.0134816

    Article  Google Scholar 

  5. Pinti, P., et al.: The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience. Ann. N. Y. Acad. Sci. 1464, 5–29 (2018). https://doi.org/10.1111/nyas.13948

    Article  Google Scholar 

  6. Piper, S.K., et al.: A wearable multi-channel fNIRS system for brain imaging in freely moving subjects. Neuroimage 85, 64–71 (2014). https://doi.org/10.1016/j.neuroimage.2013.06.062

    Article  Google Scholar 

  7. Balardin, J.B., et al.: Imaging brain function with functional near-infrared spectroscopy in unconstrained environments. Front. Hum. Neurosci. 11, 1–7 (2017). https://doi.org/10.3389/fnhum.2017.00258

    Article  Google Scholar 

  8. Naseer, N., Hong, K.-S.: fNIRS-based brain-computer interfaces: a review. Front. Hum. Neurosci. 9, 1–15 (2015). https://doi.org/10.3389/fnhum.2015.00003

    Article  Google Scholar 

  9. Kohl, S.H., Mehler, D.M.A., Lührs, M., Thibault, R.T., Konrad, K., Sorger, B.: The Potential of functional near-infrared spectroscopy-based neurofeedback—a systematic review and recommendations for best practice. Front. Neurosci. 14, 594 (2020). https://doi.org/10.3389/fnins.2020.00594

    Article  Google Scholar 

  10. Ninaus, M., et al.: Neurofeedback and serious games: In: Connolly, T.M., Hainey, T., Boyle, E., Baxter, G., Moreno-Ger, P. (eds.) Psychology, Pedagogy, and Assessment in Serious Games, pp. 82–110. IGI Global (2014). https://doi.org/10.4018/978-1-4666-4773-2.ch005

    Chapter  Google Scholar 

  11. Brigadoi, S., et al.: Motion artifacts in functional near-infrared spectroscopy: a comparison of motion correction techniques applied to real cognitive data. Neuroimage 85, 181–191 (2014). https://doi.org/10.1016/j.neuroimage.2013.04.082

    Article  Google Scholar 

  12. Cooper, R.J., et al.: A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy. Front. Neurosci. 6, 1–10 (2012). https://doi.org/10.3389/fnins.2012.00147

    Article  Google Scholar 

  13. 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 

  14. Cui, X., Bray, S., Reiss, A.L.: Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics. Neuroimage 49, 3039–3046 (2010). https://doi.org/10.1016/j.neuroimage.2009.11.050

    Article  Google Scholar 

  15. Cui, X., Bray, S., Bryant, D.M., Glover, G.H., Reiss, A.L.: A quantitative comparison of NIRS and fMRI across multiple cognitive tasks. Neuroimage 54, 2808–2821 (2011). https://doi.org/10.1016/j.neuroimage.2010.10.069

    Article  Google Scholar 

  16. Gagnon, L., Cooper, R.J., Yücel, M.A., Perdue, K.L., Greve, D.N., Boas, D.A.: Short separation channel location impacts the performance of short channel regression in NIRS. Neuroimage 59, 2518–2528 (2012). https://doi.org/10.1016/j.neuroimage.2011.08.095

    Article  Google Scholar 

  17. Brigadoi, S., Cooper, R.J.: How short is short? Optimum source–detector distance for short-separation channels in functional near-infrared spectroscopy. Neurophotonics 2, 025005 (2015). https://doi.org/10.1117/1.NPh.2.2.025005

    Article  Google Scholar 

  18. Franceschini, M.A., Fantini, S., Thompson, J.H., Culver, J.P., Boas, D.A.: Hemodynamic evoked response of the sensorimotor cortex measured noninvasively with near-infrared optical imaging. Psychophysiology 40, 548–560 (2003). https://doi.org/10.1111/1469-8986.00057

    Article  Google Scholar 

  19. Robinson, N., et al.: Real-time subject-independent pattern classification of overt and covert movements from fNIRS signals. PLoS One 11, 1–21 (2016). https://doi.org/10.1371/journal.pone.0159959

    Article  Google Scholar 

  20. Carius, D., Seidel-Marzi, O., Kaminski, E., Lisson, N., Ragert, P.: Characterizing hemodynamic response alterations during basketball dribbling. PLoS One 15, e0238318 (2020). https://doi.org/10.1371/journal.pone.0238318

    Article  Google Scholar 

  21. Singh, A.K., Dan, I.: Exploring the false discovery rate in multichannel NIRS. Neuroimage 33, 542–549 (2006). https://doi.org/10.1016/j.neuroimage.2006.06.047

    Article  Google Scholar 

  22. Oldfield, R.C.: The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9, 97–113 (1971). https://doi.org/10.1016/0028-3932(71)90067-4

    Article  Google Scholar 

  23. Peirce, J., et al.: PsychoPy2: experiments in behavior made easy. Behav. Res. Methods 51(1), 195–203 (2019). https://doi.org/10.3758/s13428-018-01193-y

    Article  Google Scholar 

  24. Collins, D.L., et al.: Design and construction of a realistic digital brain phantom. IEEE Trans. Med. Imaging 17, 463–468 (1998). https://doi.org/10.1109/42.712135

    Article  Google Scholar 

  25. Aasted, C.M., et al.: Anatomical guidance for functional near-infrared spectroscopy: atlasviewer tutorial. Neurophotonics. 2, 020801 (2015). https://doi.org/10.1117/1.NPh.2.2.020801

    Article  Google Scholar 

  26. Huppert, T.J., Diamond, S.G., Franceschini, M.A., Boas, D.A.: HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain. Appl. Opt. 48, D280 (2009). https://doi.org/10.1364/AO.48.00D280

    Article  Google Scholar 

  27. Tzourio-Mazoyer, N., et al.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273–289 (2002). https://doi.org/10.1006/nimg.2001.0978

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Psychology, University of Graz, Universitaetsplatz 2/III, 8010, Graz, Austria

    Thomas Kanatschnig, Guilherme Wood & Silvia Erika Kober

  2. BioTechMed-Graz, Mozartgasse 12/II, 8010, Graz, Austria

    Guilherme Wood & Silvia Erika Kober

Authors
  1. Thomas Kanatschnig
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  2. Guilherme Wood
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  3. Silvia Erika Kober
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Corresponding author

Correspondence to Thomas Kanatschnig .

Editor information

Editors and Affiliations

  1. NATO STO, La Spezia, Italy

    Francesca de Rosa

  2. Le Mans University, Le Mans Cedex 9, France

    Iza Marfisi Schottman

  3. HPU, Royal Institute of Technology, Södertälje, Stockholms Län, Sweden

    Jannicke Baalsrud Hauge

  4. University of Genoa, Genoa, Italy

    Francesco Bellotti

  5. Dublin Institute of Technology, Dublin, Ireland

    Pierpaolo Dondio

  6. Université Côte d'Azur, Nice, France

    Margarida Romero

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Kanatschnig, T., Wood, G., Kober, S.E. (2021). The Potential of Functional Near-Infrared Spectroscopy (fNIRS) for Motion-Intensive Game Paradigms. In: de Rosa, F., Marfisi Schottman, I., Baalsrud Hauge, J., Bellotti, F., Dondio, P., Romero, M. (eds) Games and Learning Alliance. GALA 2021. Lecture Notes in Computer Science(), vol 13134. Springer, Cham. https://doi.org/10.1007/978-3-030-92182-8_9

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  • DOI: https://doi.org/10.1007/978-3-030-92182-8_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-92181-1

  • Online ISBN: 978-3-030-92182-8

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