Cognitive load associations when utilizing auditory display within image-guided neurosurgery

  • Joseph Plazak
  • Daniel A. DiGiovanni
  • D. Louis Collins
  • Marta Kersten-OertelEmail author
Original Article



The combination of data visualization and auditory display (e.g., sonification) has been shown to increase accuracy, and reduce perceived difficulty, within 3D navigation tasks. While accuracy within such tasks can be measured in real time, subjective impressions about the difficulty of a task are more elusive to obtain. Prior work utilizing electrophysiology (EEG) has found robust support that cognitive load and working memory can be monitored in real time using EEG data.


In this study, we replicated a 3D navigation task (within the context of image-guided surgery) while recording data pertaining to participants’ cognitive load through the use of EEG relative alpha-band weighting data. Specifically, 13 subjects navigated a tracked surgical tool to randomly placed 3D virtual locations on a CT cerebral angiography volume while being aided by visual, aural, or both visual and aural feedback. During the study EEG data were captured from the participants, and after the study a NASA TLX questionnaire was filled out by the subjects. In addition to replicating an existing experimental design on auditory display within image-guided neurosurgery, our primary aim sought to determine whether EEG-based markers of cognitive load mirrored subjective ratings of task difficulty


Similar to existing literature, our study found evidence consistent with the hypothesis that auditory display can increase the accuracy of navigating to a specified target. We also found significant differences in cognitive working load across different feedback modalities, but none of which supported the experiments hypotheses. Finally, we found mixed results regarding the relationship between real-time measurements of cognitive workload and a posteriori subjective impressions of task difficulty.


Although we did not find a significant correlation between the subjective and physiological measurements, differences in cognitive working load were found. As well, our study further supports the use of auditory display in image-guided surgery.


Image-guided neurosurgery Neuronavigation Auditory display Data sonification EEG Cognitive workload Evaluation Interfaces 



This study was funded by Natural Sciences and Engineering Research Council of Canada (NSERC Grant N0759) and Fonds de recherche du Quebec Nature et technologies (FRQNT Grant F01296).

Compliances with ethical standard

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

Supplementary material 1 (avi 22397 KB)


  1. 1.
    Hermann T (2008) Taxonomy and definitions for sonification and auditory display. International Community for Auditory Display, ChicagoGoogle Scholar
  2. 2.
    Kramer G, Walker B, Bonebright T, Cook P, Flowers JH, Miner N, Neuhoff J (2010) Sonification report: status of the field and research agenda. Technical Report. International Community for Auditory Display, 1999. Accessed Jan 2019
  3. 3.
    Cherry EC (1953) Some experiments on the recognition of speech, with one and with two ears. J Acoust Soc Am 25(5):975–979CrossRefGoogle Scholar
  4. 4.
    Hansen C, Black D, Lange C, Rieber F, Lamadé W, Donati M, Hahn HK (2013) Auditory support for resection guidance in navigated liver surgery. Int J Med Robot Comput Assist Surg 9(1):36–43CrossRefGoogle Scholar
  5. 5.
    Willems PWA, Noordmans HJ, van Overbeeke JJ, Viergever MA, Tulleken CAF, van der Sprenkel JB (2005) The impact of auditory feedback on neuronavigation. Acta Neurochir 147(2):167–173CrossRefGoogle Scholar
  6. 6.
    Black D, Hansen C, Nabavi A, Kikinis R, Hahn H (2017) A Survey of auditory display in image-guided interventions. Int J Comput Assist Radiol Surg 1–12:1665CrossRefGoogle Scholar
  7. 7.
    Bork F, Fuers B, Schneider AK, Pinto F, Graumann C, Navab N (2015) Auditory and visio-temporal distance coding for 3-dimensional perception in medical augmented reality. In: IEEE international symposium on mixed and augmented reality (ISMAR), pp 7–12Google Scholar
  8. 8.
    Wendt F, Zotter F, Frank M, Höldrich R (2017) Auditory distance control using a variable-directivity loudspeaker. Appl Sci 7(7):666CrossRefGoogle Scholar
  9. 9.
    Anderson EW, Potter KC, Matzen LE, Shepherd JF, Preston GA, Silva CT (2011) A user study of visualization effectiveness using EEG and cognitive load. Comput Graph Forum 30(3):791–800CrossRefGoogle Scholar
  10. 10.
    Hart SG, Staveland LE (1988) Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. Adv Psychol 52:139–183CrossRefGoogle Scholar
  11. 11.
    Hankins TC, Wilson GF (1998) A comparison of heart rate, eye activity, EEG and subjective measures of pilot mental workload during flight. Aviat Space Environ Med 69(4):360–367Google Scholar
  12. 12.
    Naismith LM, Cheung JJ, Ringsted C, Cavalcanti RB (2015) Limitations of subjective cognitive load measures in simulationbased procedural training. Med Educ 49(8):805–814CrossRefGoogle Scholar
  13. 13.
    Annett J (2002) Subjective rating scales: science or art? Ergonomics 45(14):966–987CrossRefGoogle Scholar
  14. 14.
    Klimesch W (1999) EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Rev 29:169195CrossRefGoogle Scholar
  15. 15.
    Yuksel BF, Oleson KB, Harrison L, Peck EM, Afergan D, Chang R, Jacob RJ (2016) Learn piano with BACh: an adaptive learning interface that adjusts task difficulty based on brain state. In: Proceedings of the 2016 CHI conference on human factors in computing systems, pp 5372–5384Google Scholar
  16. 16.
    Michels L, Moazami-Goudarzi M, Jeanmonod D, Sarnthein J (2008) EEG alpha distinguishes between cuneal and precuneal activation in working memory. Neuroimage 40(3):1296–1310CrossRefGoogle Scholar
  17. 17.
    Jensen O, Gelfand J, Kounios J, Lisman JE (2002) Oscillations in the alpha band (912 Hz) increase with memory load during retention in a short-term memory task. Cereb Cortex 12(8):877–882CrossRefGoogle Scholar
  18. 18.
    Jensen O, Tesche CD (2002) Frontal theta activity in humans increases with memory load in a working memory task. Eur J Neurosci 15(8):1395–1399CrossRefGoogle Scholar
  19. 19.
    Muse Developers (2015). Accessed Nov 2018
  20. 20.
    Roux F, Uhlhaas PJ (2014) Working memory and neural oscillations: alphagamma versus thetagamma codes for distinct WM information? Trends Cogn Sci 18(1):16–25CrossRefGoogle Scholar
  21. 21.
    Drouin S, Kochanowska A, Kersten-Oertel M, Gerard IJ, Zelmann R, De Nigris D, Hall JA, Sinclair D, Petrecca K, Del Maestro R, Collins DLIBIS (2017) An OR ready open-source platform for image-guided neurosurgery. Int J Comput Assist Radiol Surg 12(3):363–378CrossRefGoogle Scholar
  22. 22.
    Puckette M (1996) Pure data: another integrated computer music environment. In: Proceedings of the second intercollege computer music concerts, pp 37–41Google Scholar
  23. 23.
    Bencina R (2006) oscpack [computer software]Google Scholar
  24. 24.
    Plazak J, Drouin S, Collins L, Kersten-Oertel M (2017) Distance sonification in image-guided neurosurgery. Healthc Technol Lett 4(5):199–203CrossRefGoogle Scholar
  25. 25.
    Krigolson OE, Williams CC, Norton A, Hassall CD, Colino FL (2017) Choosing MUSE: validation of a low-cost, portable EEG system for ERP research. Front Neurosci 11:109CrossRefGoogle Scholar
  26. 26.
    Negi S, Mitra R (2018) EEG metrics to determine cognitive load and affective states: a pilot study. In: Proceedings of the 2018 ACM international joint conference and 2018 international symposium on pervasive and ubiquitous computing and wearable computers, pp 182–185Google Scholar
  27. 27.
    Brooke J (1996) SUS—a quick and dirty usability scale. Usability Eval Ind 189(194):4–7Google Scholar
  28. 28.
    Berkman MI, Karahoca D (2016) Re-assessing the usability metric for user experience (UMUX) scale. J Usability Stud 11(3):89–109Google Scholar
  29. 29.
    Lewis JR, Utesch BS, Maher DE (2013) UMUX-LITE: when there’s no time for the SUS. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 2099–2102. ACMGoogle Scholar
  30. 30.
    Tomlinson BJ, Noah BE, Walker BN (2018) BUZZ: an auditory interface user experience scale. In: Extended abstracts of the 2018 CHI conference on human factors in computing systems, p LBW096. ACMGoogle Scholar

Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.Gina Cody School of Engineering and Computer ScienceConcordia UniversityMontrealCanada
  2. 2.Montreal Neurological InstituteMcGill UniversityMontrealCanada

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