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Using fNIRS to Measure Mental Workload in the Real World

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Advances in Physiological Computing

Part of the book series: Human–Computer Interaction Series ((HCIS))

Abstract

In the past decade, functional near-infrared spectroscopy (fNIRS) has seen increasing use as a non-invasive brain sensing technology. Using optical signals to approximate blood-oxygenation levels in localized regions of the brain, the appeal of the fNIRS signal is that it is relatively robust to movement artifacts and comparable to fMRI measures. We provide an overview of research that builds towards the use of fNIRS to monitor user workload in real world environments, and eventually to act as input to biocybernetic systems. While there are still challenges for the use of fNIRS in real world environments, its unique characteristics make it an appealing alternative for monitoring the cognitive processes of a user.

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References

  • Ayaz H, Shewokis PA, Bunce S, Izzetoglu K, Willems B, Onaral B (2012) Optical brain monitoring for operator training and mental workload assessment. NeuroImage 59(1):36–47

    Google Scholar 

  • Baddeley AD (1992) Working memory. Science 255(5044):556–559

    Article  Google Scholar 

  • Bor D, Cumming N, Scott CEL, Owen AM (2004) Prefrontal cortical involvement in verbal encoding strategies. Eur J Neurosci 19:3365–3370

    Article  Google Scholar 

  • Bor D, Duncan J, Wiseman RJ, Owen AM (2003) Encoding strategies dissociate prefrontal activity from working memory demand. Neuron 37:361–367

    Article  Google Scholar 

  • Braver TS, Cohen JD, Nystrom LE, Jonides J, Smith EE, Noll DC (1997) A parametric study of prefrontal cortex involvement in human working memory. Neuroimage 5:49–62

    Article  Google Scholar 

  • Bunce SC, Izzetoglu K, Ayaz H, Shewokis P, Izzetoglu M, Pourrezaei K, Onaral B (2011) Implementation of fNIRS for monitoring levels of expertise and mental workload. In: Foundations of Augmented Cognition. Directing the Future of Adaptive Systems. Springer, Berlin, pp 13–22

    Google Scholar 

  • Burgess PW, Quayle A, Frith CD (2001) Brain regions involved in prospective memory as determined by positron emission tomography. Neuropsychologia 39:545–555

    Article  Google Scholar 

  • Cahill L, Uncapher M, Kilpatrick L, Alkire MT, Turner J (2004) Sex-related hemispheric lateralization of amygdala function in emotionally influenced memory: an fMRI investigation. Learn Mem 11(3):261–266

    Article  Google Scholar 

  • Chance B, Anday E, Nioka S, Zhou S, Hong L, Worden K, Li C et al (1998) A novel method for fast imaging of brain function, non-invasively, with light. Opt Express 2(10):41123

    Article  Google Scholar 

  • Christoff K, Gabrieli JDE (2000) The frontopolar cortex and human cognition: evidence for a rostrocaudal hierarchical organisation within the human prefrontal cortex. Psychobiology 28:168–186

    Google Scholar 

  • Christoff K, Prabhakaran V, Dorfman J, Zhao Z, Kroger JK, Holyoak KJ, Gabrieli JD (2001) Rostrolateral prefrontal cortex involvement in relational integration during reasoning. Neuroimage 14(5):1136–1149

    Google Scholar 

  • Cleveland WS, McGill R (1984) Graphical Perception: Theory, experimentation, and the application to the development of graphical methods. J Am Stat Assoc 387:531–554

    Article  MathSciNet  Google Scholar 

  • Cohen JD, Perlstein WM, Braver TS, Nystrom LE, Noll DC, Jonides J, Smith EE (1997) Temporal dynamics of brain activation during a working memory task. Nature 386:604–608

    Article  Google Scholar 

  • Cui X, Bray S, Reiss A (2010) Speeded near infrared spectroscopy (NIRS) response detection. PLoS One 5(11):e15474

    Article  Google Scholar 

  • Davis MH, Meunier F, Marslen-Wilson WD (2004) Neural responses to morphological, syntactic, and semantic properties of single words: an fMRI study. Brain Lang 89(3):439–449

    Article  Google Scholar 

  • D’Esposito M, Zarahn E, Aguirre G (1999) Event-related functional MRI: implications for cognitive psychology. Psychol Bull 125(1):155–164

    Article  Google Scholar 

  • Dove A, Rowe JB, Brett M, Owen AM (2001) Neural correlates of passive and active encoding and retrieval: a 3T fMRI study. Neuroimage 13(Suppl):660

    Article  Google Scholar 

  • Franceschini MA, Joseph DK, Huppert TJ, Diamond SG, Boas DA (2006) Diffuse optical imaging of the whole head. J Biomed Opt 11(5):054007

    Article  Google Scholar 

  • Gevins AS, Cutillo BC (1993) Neuroelectric evidence for distributed processing in human working memory. Electroencephalogr Clin Neurophysiol 87:128–143

    Article  Google Scholar 

  • Girouard A, Solovey E, Hirshfield L, Chauncey K, Sassaroli A, Fantini S, Jacob RJK (2009) Distinguishing difficulty levels with non-invasive brain activity measurements. Interact 2009:440–452

    Google Scholar 

  • Gore JC (2003) Principles and practice of functional MRI of the human brain. J Clin Investig 112(1):4–9

    Article  Google Scholar 

  • Grabenhorst F, Rolls ET (2011) Value, pleasure and choice in the ventral prefrontal cortex. Trends Cogn Sci 15(2):5667

    Article  Google Scholar 

  • Herff C, Heger D, Putze F, Guan C, Schultz T (2012) Cross-subject classification of speaking modes using fNIRS. ICONIP 2012:417–424

    Google Scholar 

  • Hirshfield LM, Solovey ET, Girouard A, Kebinger J, Jacob RJK, Sassaroli A, Fantini S (2009) Brain measurement for usability testing and adaptive interfaces: an example of uncovering syntactic workload with functional near infrared spectroscopy. In: CHI 2009

    Google Scholar 

  • Hirshfield LM, Gulotta R, Hirshfield S, Hincks S, Russell M, Ward R, Williams T, Jacob RJK (2011) This is your brain on interfaces: enhancing usability testing with functional near-infrared spectroscopy. In: CHI 2011

    Google Scholar 

  • Hockey GRJ (1997) Compensatory control in the regulation of human performance under stress and high workload: a cognitive-energetical framework. Biol Psychol 45:73–93

    Article  Google Scholar 

  • Izzetoglu K, Ayaz H, Menda J (2011) Applications of functional near infrared imaging: case study on UAV ground controller. In: Schmorrow DD, Fidopiastis CM (eds) Foundations of augmented cognition. Springer, New York, pp 608–617

    Google Scholar 

  • Jonides J, Smith EE, Koeppe RA, Awh E, Minoshima S, Mintun MA (1993) Spatial working memory in humans as revealed by PET. Nature 363:623–625

    Article  Google Scholar 

  • Koechlin E, Corrado G, Pietrini P, Grafman J (2000) Dissociating the role of the medial and lateral anterior prefrontal cortex in human planning. Proc Nat Acad Sci. 97(13):7651–7656

    Google Scholar 

  • Kroger JK, Sabb FW, Fales CL, Bookheimer SY, Cohen MS, Holyoak KJ (2002) Recruitment of anterior dorsolateral prefrontal cortex in human reasoning: a parametric study of relational complexity. Cereb Cortex 12:477–485

    Google Scholar 

  • Liu T, Saito H, Oi M (2012) Distinctive activation patterns under intrinsically versus extrinsically driven cognitive loads in prefrontal cortex: a near-infrared spectroscopy study using a driving video game. Neuroscience letters, 506(2):220–224

    Google Scholar 

  • Luu S, Chau T (2008) Decoding subjective preference from single-trial near-infrared spectroscopy signals. J Neural Eng 6:058001

    Google Scholar 

  • Miller G (1956) The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev 63(2):8197

    Google Scholar 

  • Minati L, Grisoli M, Franceschetti S, Epifani F, Granvillano A, Medford N, Harrison N et al (2012) Neural signatures of economic parameters during decision-making: a functional MRI (FMRI), electroencephalography (EEG) and autonomic monitoring study. Brain Topogr 25(1):73–96

    Google Scholar 

  • Moghimi S, Kushki A, Power S, Guerguerian AM, Chau T (2012) Automatic detection of a prefrontal cortical response to emotionally rated music using multi-channel near-infrared spectroscopy. J Neural Eng 9(2):026022

    Google Scholar 

  • Owen AM, McMillan KM, Laird AR, Bullmore E (2005) N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies. Hum Brain Mapp 25(1):46–59

    Article  Google Scholar 

  • Peck EM, Afergan D, Jacob RJK (2013a) Investigation of fNIRS brain sensing as input to information filtering systems. In: Augmented human 2013

    Google Scholar 

  • Peck EM, Yuksel BF, Ottley A, Jacob RJK, Chang R (2013b) Using fNIRS brain sensing to evaluate information visualization interfaces. In: CHI 2013

    Google Scholar 

  • Ramnani N, Owen AM (2004) Anterior prefrontal cortex: insights into function from anatomy and neuroimaging. Nat Rev Neurosci 5:184–194

    Article  Google Scholar 

  • Repovš G, Baddeley A (2006) The multi-component model of working memory: explorations in experimental cognitive psychology. Neuroscience 139:5–21

    Article  Google Scholar 

  • Repovš G, Bresjanac M (2006) Cognitive neuroscience of working memory: a prologue. Neuroscience 139:1–3

    Article  Google Scholar 

  • Rugg MD, Fletcher PC, Allan K, Frith CD, Frackowiak RS, Dolan RJ (1998) Neural correlates of memory retrieval during recognition memory and cued recall. Neuroimage 8:262–273

    Article  Google Scholar 

  • Sase I, Takatsuki A, Seki J, Yanagida T, Seiyama A (2012) Noncontact backscatter-mode near-infrared time-resolved imaging system: preliminary study for functional brain mapping. J Biomed Opt 11(5):054006

    Google Scholar 

  • Solovey ET, Girouard A, Chauncey K, Hirshfield LM, Sassaroli A, Zheng F, Fantini S, Jacob RJK (2009) Using fNIRS brain sensing in realistic HCI settings: experiments and guidelines. In: UIST 2009

    Google Scholar 

  • Solovey ET, Lalooses F, Chauncey K, Weaver D, Scheutz M, Sassaroli A, Fantini S, Jacob RJK (2011) Sensing cognitive multitasking for a brain-based adaptive user interface. In: CHI 2011

    Google Scholar 

  • Solovey ET, Schermerhorn P, Scheutz M, Sassaroli A, Fantini S, Jacob RJK (2012) Brainput: enhancing interactive systems with streaming fNIRS brain input. In: CHI 2012

    Google Scholar 

  • Strangman G, Culver JP, Thompson JH, Boas DA (2002) A quantitative comparison of simultaneous BOLD fMRI and NIRS recordings during functional brain activation. NeuroImage 17(2):719731

    Article  Google Scholar 

  • Tsunashima H, Yanagisawa K (2009) Measurement of brain function of car driver using functional near-infrared spectroscopy (fNIRS). Comput Intell Neurosci 2009:164958

    Google Scholar 

  • Tulving E (1983) Elements of episodic memory. Clarendon, Oxford

    Google Scholar 

  • Wickens CD (2002) Multiple resources and performance prediction. Theor Issues Ergon Sci 3:159–177

    Google Scholar 

  • Wildey C, MacFarlane D, Khan B, Tian F, Liu H, Alexandrakis G (2010) Improved fNIRS using a novel brush optrode. In: Laser science

    Google Scholar 

  • Vidaurre C, Sannelli C, Muller K-R, Blankertz B (2010) Machine-learning-based coadaptive calibration for brain-computer interfaces. Neural Comput 816:791816

    Google Scholar 

  • Villringer A, Chance B (1997) Non-invasive optical spectroscopy and imaging of human brain function. Trends Neurosci 20(10):43542

    Article  Google Scholar 

  • Yurtsever G, Ayaz H, Kepics F, Onaral B (2003) Wireless, continuous wave near infrared spectroscopy system for monitoring brain activity. In: Bioengineering conference, pp 53–53

    Google Scholar 

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Authors and Affiliations

  1. Tufts University, Medford, USA

    Evan M. Peck, Daniel Afergan, Beste F. Yuksel, Francine Lalooses & Robert J. K. Jacob

Authors
  1. Evan M. Peck
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  2. Daniel Afergan
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  3. Beste F. Yuksel
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  4. Francine Lalooses
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  5. Robert J. K. Jacob
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Corresponding author

Correspondence to Evan M. Peck .

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Editors and Affiliations

  1. Liverpool John Moores University School of Natural Sciences & Psychology, Liverpool, Merseyside, United Kingdom

    Stephen H. Fairclough

  2. Liverpool John Moores University School of Natural Sciences & Psychology, Liverpool, Merseyside, United Kingdom

    Kiel Gilleade

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Peck, E.M., Afergan, D., Yuksel, B.F., Lalooses, F., Jacob, R.J.K. (2014). Using fNIRS to Measure Mental Workload in the Real World. In: Fairclough, S., Gilleade, K. (eds) Advances in Physiological Computing. Human–Computer Interaction Series. Springer, London. https://doi.org/10.1007/978-1-4471-6392-3_6

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  • DOI: https://doi.org/10.1007/978-1-4471-6392-3_6

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  • Print ISBN: 978-1-4471-6391-6

  • Online ISBN: 978-1-4471-6392-3

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