Does Feedback Design Matter? A Neurofeedback Study Comparing Immersive Virtual Reality and Traditional Training Screens in Elderly

  • Silvia Erika Kober Department of Psychology, University of Graz, Austria BioTechMed-Graz
  • Johanna Louise Reichert Department of Psychology, University of Graz, Austria BioTechMed-Graz
  • Daniela Schweiger Department of Psychology, University of Graz, Austria
  • Christa Neuper Department of Psychology, University of Graz, Austria BioTechMed-Graz Laboratory of Brain-Computer Interfaces, Institute of Neural Engineering, Graz University of Technology
  • Guilherme Wood Department of Psychology, University of Graz, Austria BioTechMed-Graz
Keywords: Aging, Brain-Computer Interface, Feedback design, User Experience,

Abstract

Neurofeedback (NF) is a Brain-Computer Interface (BCI) application, in which the brain activity is fed back to the user in real-time enabling voluntary brain control. In this context, the significance of the feedback design is mainly unexplored. Highly immersive feedback scenarios using virtual reality (VR) technique are available. However, their effects on subjective user experience as well as on objective outcome measures remain open. In the present article, we discuss the general pros and cons of using VR as feedback modality in BCI applications. Furthermore, we report on the results of an empirical study, in which the effects of traditional two-dimensional and three-dimensional VR based feedback scenarios on NF training performance and user experience in healthy older individuals and neurologic patients were compared. In conclusion, we suggest indications and contraindications of immersive VR feedback designs in BCI applications. Our results show that findings in healthy individuals are not always transferable to patient populations having an impact on serious game and feedback design.

References

[1] Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., Vaughan, T. M., Brain–computer interfaces for communication and control. Clinical Neurophysiology 2002, 113, 767–791. https://doi.org/10.1016/S1388-2457(02)00057-3
[2] Gruzelier, J. H., EEG-neurofeedback for optimising performance. I: A review of cognitive and affective outcome in healthy participants. Neuroscience & Biobehavioral Reviews 2014, 44, 124–141. https://doi.org/10.1016/j.neubiorev.2013.09.015
[3] Kober, S. E., Witte, M., Ninaus, M., Neuper, C., Wood, G., Learning to modulate one's own brain activity: the effect of spontaneous mental strategies. Front. Hum. Neurosci 2013, 7. https://doi.org/10.3389/fnhum.2013.00695
[4] Wood, G., Kober, S. E., Witte, M., Neuper, C., On the need to better specify the concept of “control” in brain-computer-interfaces/neurofeedback research. Frontiers in Systems Neuroscience 2014, 8.
[5] Kropotov, J. D., Quantitative EEG, event-related potentials and neurotherapy, Elsevier/Academic, Amsterdam, Boston, London 2009.
[6] Kober, S. E., Schweiger, D., Witte, M., Reichert, J. L. et al., Specific effects of EEG based neurofeedback training on memory functions in post-stroke victims. Journal of Neuroengineering and Rehabilitation 2015, 12, 107. https://doi.org/10.1186/s12984-015-0105-6
[7] Allison, B., Neuper, C., in: Tan, D., Nijholt, A. (Eds.), Brain-Computer Interfaces: Human-Computer Interaction Series, Springer-Verlag, London 2010, pp. 35–54. https://doi.org/10.1007/978-1-84996-272-8_3
[8] Blankertz, B., Sannelli, C., Halder, S., Hammer, E. M. et al., Neurophysiological predictor of SMR-based BCI performance. NeuroImage 2010, 51, 1303–1309. https://doi.org/10.1016/j.neuroimage.2010.03.022
[9] Kübler, A., Neumann, N., Wilhelm, B., Hinterberger, T., Birbaumer, N., Predictability of Brain-Computer Communication. Journal of Psychophysiology 2004, 18, 121–129. https://doi.org/10.1027/0269-8803.18.23.121
[10] Halder, S., Varkuti, B., Bogdan, M., Kübler, A. et al., Prediction of brain-computer interface aptitude from individual brain structure. Front. Hum. Neurosci 2013, 7, 1–9. https://doi.org/10.3389/fnhum.2013.00105
[11] Reichert, J. L., Kober, S. E., Neuper, C., Wood, G., Resting-state sensorimotor rhythm (SMR) power predicts the ability to up-regulate SMR in an EEG-instrumental conditioning paradigm. Clin Neurophysiol 2015, 126, 2068–2077. https://doi.org/10.1016/j.clinph.2014.09.032
[12] Ninaus, M., Kober, S., Witte, M., Koschutnig, K. et al., Neural substrates of cognitive control under the belief of getting neurofeedback training. Frontiers in Human Neuroscience 2013, 7, 1–10. https://doi.org/10.3389/fnhum.2013.00914
[13] Ninaus, M., Kober, S., Witte, M., Koschutnig, K. et al., Brain volumetry and self-regulation of brain activity relevant for neurofeedback. Biological Psychology 2015, 110, 126–133. https://doi.org/10.1016/j.biopsycho.2015.07.009
[14] Nijboer, F., Furdea, A., Gunst, I., Mellinger, J. et al., An auditory brain–computer interface (BCI). Journal of Neuroscience Methods 2008, 167, 43–50. https://doi.org/10.1016/j.jneumeth.2007.02.009
[15] Kleih, S., Nijboer, F., Halder, S., Kübler, A., Motivation modulates the P300 amplitude during brain–computer interface use. Clinical Neurophysiology 2010, 121, 1023–1031. https://doi.org/10.1016/j.clinph.2010.01.034
[16] Hammer, E. M., Halder, S., Blankertz, B., Sannelli, C. et al., Psychological predictors of SMR-BCI performance. Biological Psychology 2012, 89, 80–86. https://doi.org/10.1016/j.biopsycho.2011.09.006
[17] Witte, M., Kober, S. E., Ninaus, M., Neuper, C., Wood, G., Control beliefs can predict the ability to up-regulate sensorimotor rhythm during neurofeedback training. Front. Hum. Neurosci 2013, 7, 1–8. https://doi.org/10.3389/fnhum.2013.00478
[18] Gruzelier, J. H., EEG-neurofeedback for optimising performance. III: A review of methodological and theoretical considerations. Neuroscience & Biobehavioral Reviews 2014. https://doi.org/10.1016/j.neubiorev.2014.03.015
[19] Arns, M., Ridder, S. de, Strehl, U., Breteler, M., Coenen, T., Efficacy of Neurofeedback treatment in ADHD: The effects on Inattention, Impulsivity and Hyperactivity: A meta-analysis. Clin EEG Neurosci. 2009, 40, 180–189. https://doi.org/10.1177/155005940904000311
[20] Tan, G., Thornby, J., Hammond, D. C., Strehl, U. et al., Meta-analysis of EEG biofeedback in treating epilepsy. Clin EEG Neurosci 2009, 40, 173–179. https://doi.org/10.1177/155005940904000310
[21] Cho, B.-H., Kim, S., Shin, D. I., Lee, J. H. et al., Neurofeedback training with virtual reality for inattention and impulsiveness. Cyberpsychology & behavior : the impact of the Internet, multimedia and virtual reality on behavior and society 2004, 7, 519–526.
[22] Ros, T., Théberge, J., Frewen, P. A., Kluetsch, R. et al., Mind over chatter: Plastic up-regulation of the fMRI salience network directly after EEG neurofeedback. NeuroImage 2013, 65, 324–335. https://doi.org/10.1016/j.neuroimage.2012.09.046
[23] Emmert, K., Kopel, R., Sulzer, J., Brühl, A. B. et al., Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: How is brain regulation mediated? NeuroImage 2016, 124, 806–812. https://doi.org/10.1016/j.neuroimage.2015.09.042
[24] Gaume, A., Vialatte, A., Mora-Sánchez, A., Ramdani, C., Vialatte, F. B., A psychoengineering paradigm for the neurocognitive mechanisms of biofeedback and neurofeedback. Neuroscience & Biobehavioral Reviews.
[25] Raymond, J., Varney, C., Parkinson, L. A., Gruzelier, J. H., The effects of alpha/theta neurofeedback on personality and mood. Cognitive Brain Research 2005, 23, 287–292. https://doi.org/10.1016/j.cogbrainres.2004.10.023
[26] Kober, S., Reichert, J., Schweiger, D., Neuper, C., Wood, G., Effects of a 3D Virtual Reality Neurofeedback Scenario on User Experience and Performance in Stroke Patients. GALA Conference 2016 proceedings 2016.
[27] Kober, S. E., Witte, M., Stangl, M., Valjamae, A. et al., Shutting down sensorimotor interference unblocks the networks for stimulus processing: An SMR neurofeedback training study. Clin Neurophysiol 2015, 126, 82–95. https://doi.org/10.1016/j.clinph.2014.03.031
[28] Yan, N., Wang, J., Liu, M., Zong, L. et al., Designing a Brain-computer Interface Device for Neurofeedback Using Virtual Environments. Journal of Medical and Biological Engineering 2008, 28, 167–172.
[29] Mercier-Ganady, J., Lotte, F., Loup-Escande, E., Marchal, M., Lecuyer, A., in:, 2014 IEEE Virtual Reality (VR) 2014, pp. 33–38.
[30] Hwang, H.-J., Kwon, K., Im, C.-H., Neurofeedback-based motor imagery training for brain–computer interface (BCI). Journal of Neuroscience Methods 2009, 179, 150–156. https://doi.org/10.1016/j.jneumeth.2009.01.015
[31] Lécuyer, A., Lotte, F., Reilly, R. B., Leeb, R. et al., Brain-Computer Interfaces, Virtual Reality, and Videogames. Computer 2008, 41, 66–72. https://doi.org/10.1109/MC.2008.410
[32] Arrouet, C., Congedo, M., Marvie, J. E., Lamarche, F. et al., Open-ViBE: a 3D Platform for Real-Time Neuroscience. Journal of Neurotherapy 2005, 9, 3–25. https://doi.org/10.1300/J184v09n01_02
[33] Harris, K., Reid, D., The Influence of Virtual Reality Play on Children'S Motivation. Canadian Journal of Occupational Therapy 2005, 72, 21–29. https://doi.org/10.1177/000841740507200107
[34] Strehl, U. (Ed.), Neurofeedback: Theoretische Grundlagen - Praktisches Vorgehen - Wissenschaftliche Evidenz, Kohlhammer, Stuttgart 2013.
[35] Benedetti, F., Volpi, N. C., Parisi, L., Sartoti, G., in: Shumaker, R., Lackey, S. (Eds.), Virtual, Augmented and Mixed Reality. Applications of Virtual and Augmented Reality, Springer International Publishing 2014, pp. 236–247.
[36] Aart, J. v., Klaver, E. et al., EEG Headset For Neurofeedback Therapy - Enabling Easy Use in the Home Environment. Proceedings of the Biosignals - International Conference on Bio-inspired Signals and Systems, Funchal 2008, 23–30.
[37] Ron-Angevin, R., Daz Estrella, A., Reyes-Lecuona, A., Development of a Brain-Computer Interface (BCI)Development of a Brain-Computer Interface (BCI)Based on Virtual Reality to Improve Training Techniques. Applied Technologies in Medicine and Neuroscience 2005, 13–20.
[38] Leeb, R., Lee, F., Keinrath, C., Scherer, R. et al., Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society 2007, 15, 473–482.
[39] Friedman, D., Leeb, R., Guger, C., Steed, A. et al., Navigating Virtual Reality by Thought: What Is It Like? Presence: Teleoperators and Virtual Environments 2007, 16, 100–110. https://doi.org/10.1162/pres.16.1.100
[40] Gruzelier, J., Inoue, A., Smart, R., Steed, A., Steffert, T., Acting performance and flow state enhanced with sensory-motor rhythm neurofeedback comparing ecologically valid immersive VR and training screen scenarios. Neuroscience Letters 2010, 480, 112–116. https://doi.org/10.1016/j.neulet.2010.06.019
[41] Bayliss, J. D., Use of the evoked potential P3 component for control in a virtual apartment. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society 2003, 11, 113–116.
[42] Kober, S. E., Ninaus, M., Friedrich, E. V., Scherer, R., in: Nam, C. S., Nijholt, A., Lotte, F. (Eds.), Brain-Computer Interfaces Handbook: Technological and Theoretical Advances, CRC Press: Taylor & Francis Group, Boca Raton, London, New York 2017, in press.
[43] Marzbani, H., Marateb, H. R., Mansourian, M., Neurofeedback: A Comprehensive Review on System Design, Methodology and Clinical Applications. Basic and clinical neuroscience 2016, 7, 143–158. https://doi.org/10.15412/J.BCN.03070208
[44] Burdea, G., Virtual Rehabilitation: Benefits and Challenges. Methods of information in medicine 2003, 42, 519–523.
[45] Leeb, R., Keinrath, C., Friedman, D., Guger, C. et al., Walking by Thinking: The Brainwaves Are Crucial, Not the Muscles! Presence: Teleoperators and Virtual Environments 2006, 15, 500–514. https://doi.org/10.1162/pres.15.5.500
[46] Alimardani, M., Nishio, S., Ishiguro, H., Humanlike robot hands controlled by brain activity arouse illusion of ownership in operators. Scientific reports 2013, 3, 1–5. https://doi.org/10.1038/srep02396
[47] Alimardani, M., Nishio, S., Ishiguro, H., Effect of biased feedback on motor imagery learning in BCI-teleoperation system. Frontiers in Systems Neuroscience 2014, 8, 52. https://doi.org/10.3389/fnsys.2014.00052
[48] Neuper, C., Scherer, R., Wriessnegger, S., Pfurtscheller, G., Motor imagery and action observation: modulation of sensorimotor brain rhythms during mental control of a brain-computer interface. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology 2009, 120, 239–247.
[49] Rizzolatti, G., Craighero, L., The mirror-neuron system. Annual review of neuroscience 2004, 27, 169–192. https://doi.org/10.1146/annurev.neuro.27.070203.144230
[50] Mulder, T., Motor imagery and action observation: cognitive tools for rehabilitation. Journal of Neural Transmission 2007, 114, 1265–1278. https://doi.org/10.1007/s00702-007-0763-z
[51] Sollfrank, T., Hart, D., Goodsell, R., Foster, J., Tan, T., 3D visualization of movements can amplify motor cortex activation during subsequent motor imagery. Frontiers in Human Neuroscience 2015, 9, 463. https://doi.org/10.3389/fnhum.2015.00463
[52] Witmer, B. G., Singer, M. J., Measuring Presence in Virtual Environments: A Presence Questionnaire. Presence: Teleoper. Virtual Environ 1998, 7, 225–240. https://doi.org/10.1162/105474698565686
[53] Slater, M., Lotto, B., Arnold, M. M., Sanchez-Vives, M. V., How we experience immersive virtual environments : the concept of presence and its measurement. Anuario de Psicología 2009, 40, 193–210.
[54] Morganti, F., Virtual interaction in cognitive neuropsychology. Studies in health technology and informatics 2004, 99, 55–70.
[55] Wagner, N., Hassanein, K., Head, M., Computer use by older adults: A multi-disciplinary review. Advancing Educational Research on Computer-supported Collaborative Learning (CSCL) through the use of gStudy CSCL Tools 2010, 26, 870–882.
[56] Brooks, J. O., Goodenough, R. R., Crisler, M. C., Klein, N. D. et al., Simulator sickness during driving simulation studies. Accident; analysis and prevention 2010, 42, 788–796. https://doi.org/10.1016/j.aap.2009.04.013
[57] Kessler, J., Markowitsch, H. J., Denzler, P., Mini Mental Status Examination MMSE: German Version, Beltz, Weinheim 1990.
[58] Klimesch, W., EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Brain Res Rev 1999, 29, 169–195. https://doi.org/10.1016/S0165-0173(98)00056-3
[59] Hofer, D., Kober, S. E., Reichert, J., Krenn, M. et al., Spezifische Effekte von EEG basiertem Neurofeedbacktraining auf kognitive Leistungen nach einem Schlaganfall: Ein nutzvolles Werkzeug für die Rehabilitation? Lernen und Lernstörungen 2014, 3, 1–19. https://doi.org/10.1024/2235-0977/a000078
[60] Rheinberg, F., Vollmeyer, R., Burns, B. D., FAM: Ein Fragebogen zur Erfassung aktuller Motivation in Lern- und Leistungssituationen. Diagnostica 2001, 47, 57–66 https://doi.org/10.1026//0012-1924.47.2.57 .
[61] Kennedy, R. S., Lane, N. E., Berbaum, K. S., Lilienthal, M. G., Simulator Sickness Questionnaire: An Enhanced Method for Quantifying Simulator Sickness. The International Journal of Aviation Psychology 1993, 3, 203–220. https://doi.org/10.1207/s15327108ijap0303_3
[62] Crawford, J., Garthwaite, P. H., Investigation of the single case in neuropsychology: confidence limits on the abnormality of test scores and test score differences. Neuropsychologia 2002, 40, 1196–1208. https://doi.org/10.1016/S0028-3932(01)00224-X
[63] Crawford, J., Garthwaite, P. H., Statistical methods for single-case studies in neuropsychology: comparing the slope of a patient's regression line with those of a control sample. Cortex 2004, 40, 533–548. https://doi.org/10.1016/S0010-9452(08)70145-X
Published
2017-09-22
How to Cite
Kober, S., Reichert, J., Schweiger, D., Neuper, C., & Wood, G. (2017). Does Feedback Design Matter? A Neurofeedback Study Comparing Immersive Virtual Reality and Traditional Training Screens in Elderly. International Journal of Serious Games, 4(3). https://doi.org/10.17083/ijsg.v4i3.167
Section
Gala Conf 2016 Special Issue

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.