Skip to main content
SpringerLink
Log in

From Auditory and Visual to Immersive Neurofeedback: Application to Diagnosis of Alzheimer’s Disease

  • Chapter
  • First Online:
Neural Computation, Neural Devices, and Neural Prosthesis

Abstract

In neurofeedback, brain waves are transformed into sounds or music, graphics, and other representations, to provide real-time information on ongoing waves and patterns in the brain. Here we present various forms of neurofeedback, including sonification, sonification in combination with visualization, and at last, immersive neurofeedback, where auditory and visual feedback is provided in a multi-sided immersive environment in which participants are completely surrounded by virtual imagery and 3D sound. Neural feedback may potentially improve the user’s (or patient’s) ability to control brain activity, the diagnosis of medical conditions, and the rehabilitation of neurological or psychiatric disorders. Several psychological and medical studies have confirmed that virtual immersive activity is enjoyable, stimulating, and can have a healing effect. As an illustration, neurofeedback is generated from electroencephalograms (EEG) of Alzheimer’s disease (AD) patients and healthy subjects. The auditory, visual, and immersive representations of Alzheimer’s EEG differ substantially from healthy EEG, potentially yielding novel diagnostic tools. Moreover, such alternative representations of AD EEG are natural and intuitive, and hence easily accessible to laymen (AD patients and family members), and can provide insight into the abnormal brainwaves associated with AD.

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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. J.F. Lubar, Neocortical dynamics: implications for understanding the role of neurofeedback and related techniques for the enhancement of attention. Appl. Psychophys. Biof. 22(2), 111–126 (1997)

    Article  CAS  Google Scholar 

  2. P. Novak, S. Daniluk, S.A. Ellias, J.M. Nazzaro, Detection of the subthalamic nucleus in microelectrographic recordings in Parkinson disease using the high-frequency (>500 Hz) neuronal background. J. Neurosurg. 106(1), 175–179 (2007)

    Article  PubMed  Google Scholar 

  3. M. Baslow, The languages of neurons: an analysis of coding mechanisms by which neurons communicate, learn and store information. Entropy 11(4), 782–797 (2009)

    Article  Google Scholar 

  4. R. Laënnec [updated: 13 August 2008; cited: 23 March 2012]. Available from: http://en.wikipedia.org/wiki/File:Rene-Theophile-Hyacinthe_Laennec_(1781–1826)_with_stethoscope.jpg

  5. J. Sterne (ed.), The Audible Past Cultural Origins of Sound Reproduction (Duke University Press, Durham, 2003)

    Google Scholar 

  6. A. Guyton, J. Hall (eds.), Textbook of Medical Physiology, 9th edn. (Saunders, Philadelphia, 1996)

    Google Scholar 

  7. X-rays, in The electromagnetic spectrum [updated: 27 March 2007; cited: 18 May 2012]. Available from: http://science.hq.nasa.gov/kids/imagers/ems/xrays.html

  8. B. Kevles (ed.), Naked to the Bone Medical Imaging in the Twentieth Century (Basic Books, New York, 1996)

    Google Scholar 

  9. D.C. Hammond, What is neurofeedback? J. Neurother. Investig. Neuromod. Neurofeedback Appl. Neurosci. 10(4), 25–36 (2006)

    Google Scholar 

  10. D. Vernon, T. Egner, N. Cooper, T. Compton, C. Neilands, A. Sheri, J. Gruzelier, The effect of training distinct neurofeedback protocols on aspects of cognitive performance. Int. J. Psychophysiol. 47(1), 75–85 (2003)

    Article  PubMed  Google Scholar 

  11. J. Lévesque, M. Beauregard, B. Mensour, Effect of neurofeedback training on the neural substrates of selective attention in children with attention-deficit/hyperactivity disorder: a functional magnetic resonance imaging study. Neurosci. Lett. 394(3), 216–221 (2006)

    Article  PubMed  Google Scholar 

  12. T. Egner, M.B. Sterman, Neurofeedback treatment of epilepsy: from basic rationale to practical application. Expert. Rev. Neurother. 6(2), 247–257 (2006)

    Article  PubMed  Google Scholar 

  13. J. Beatty, A. Greenberg, W. Deibler, J. O’Hanlon, Operant control of occipital theta rhythm affects performance in a radar monitoring task. Science 183, 871–873 (1974)

    Article  CAS  PubMed  Google Scholar 

  14. H.W. Rasey, J.E. Lubar, A. McIntyre, A.C. Zoffuto, P.L. Abbott, EEG biofeedback for the enhancement of attentional processing in normal college students. J. Neurother. 1, 15–21 (1996)

    Article  Google Scholar 

  15. E. Angelakis, S. Stathopoulou, J. Frymiare, D. Green, J. Lubar, J. Kounios, EEG neurofeedback: a brief overview and an example of peak alpha frequency training for cognitive enhancement in the elderly. Clin. Neuropsychol. 21, 110–129 (2007)

    Article  PubMed  Google Scholar 

  16. S. Hanslmayr, P. Sauseng, M. Doppelmayr, M. Schabus, W. Klimesch, Increasing individual upper alpha power by neurofeedback improves cognitive performance in human subjects. Appl. Psychophys. Biof. 30(1), 1–10 (2005)

    Article  Google Scholar 

  17. J.F. Lubar, M.O. Swartwood, J.N. Swartwood, P.H. O’Donnell, Evaluation of the effectiveness of EEG neurofeedback training for ADHD in a clinical setting as measured by changes in T.O.V.A. scores, behavioral ratings, and WISC-R performance. Appl. Psychophys. Biof. 20(1), 83–99 (1995)

    CAS  Google Scholar 

  18. P.G. Swingle, Neurofeedback treatment of pseudoseizure disorder. Biol. Psychiatry 44(11), 1196–1199 (1998)

    Article  CAS  PubMed  Google Scholar 

  19. E. Baehr, J. Rosenfeld, R. Baehr, The clinical use of an alpha asymmetry protocol in the neurofeedback treatment of depression. Two case studies. J. Neurother. 2(3), 10–23 (1998)

    Article  Google Scholar 

  20. D. Hammond, Neurofeedback treatment of depression and anxiety. J. Adult Dev. 12(2), 131–137 (2005)

    Article  Google Scholar 

  21. A.S. Bolea, Neurofeedback treatment of chronic inpatient schizophrenia. J. Neurother. 14(1), 47–54 (2010)

    Article  Google Scholar 

  22. J.A. Putman, S. Othmer, Phase sensitivity of bipolar EEG training protocols. J. Neurother. 10(1), 73–79 (2006)

    Article  Google Scholar 

  23. L. Subramanian, J.V. Hindle, S. Johnston, M.V. Roberts, M. Husain, R. Goebel, D. Linden, Real-time functional magnetic resonance imaging neurofeedback for treatment of Parkinson’s disease. J. Neurosci. 31(45), 16309–16317 (2011)

    Article  CAS  PubMed  Google Scholar 

  24. C. Murray, E. Patchick, S. Pettifer, F. Caillette, T. Howard, Immersive virtual reality as a rehabilitative technology for phantom limb experience: a protocol. CyberPsychol. Behav. 9(2), 167–170 (2006)

    Article  PubMed  Google Scholar 

  25. C. Murray, S. Pettifer, T. Howard, E. Patchick, F. Caillette, J. Kulkarni, The treatment of phantom limb pain using immersive virtual reality: three case studies. Disabil. Rehabil. 29, 1465–9 (2007)

    Article  PubMed  Google Scholar 

  26. T. Egner, J.H. Gruzelier, EEG biofeedback of low beta band components: frequency-specific effects on variables of attention and event-related brain potentials. Clin. Neurophysiol. 115(1), 131–139 (2004)

    Article  CAS  PubMed  Google Scholar 

  27. F. Nijboer, A. Furdea, I. Gunst, J. Mellinger, D.J. McFarland, N. Birbaumer, A. Kübler, An auditory brain–computer interface (BCI). J. Neurosci. Methods 167(1), 43–50 (2008)

    Article  PubMed  Google Scholar 

  28. M. Elgendi, B. Rebsamen, A. Cichocki, F. Vialatte, J. Dauwels, Real-time wireless sonification of brain signals, in Proceedings of The International Conference on Cognitive Neurodynamics (ICCN2011), Japan, 9–13 June 2011

    Google Scholar 

  29. T. Rutkowski, F. Vialatte, A. Cichocki, D. Mandic, A. Barros, Auditory feedback for brain computer interface management – an EEG data sonification approach, in Knowledge-Based Intelligent Information and Engineering Systems, ed. by B. Gabrys, R. Howlett, L. Jain. Lecturer Notes in Computer Science, vol. 4253 (Springer, Berlin, 2006), pp. 1232–1239

    Chapter  Google Scholar 

  30. T. Hinterberger, G. Baier, Parametric orchestral sonification of EEG in real time. IEEE Multimedia 12(2), 70–79 (2005)

    Article  Google Scholar 

  31. Q. Wang, O. Sourina, M. Nguyen, Fractal dimension based neurofeedback in serious games. Vis. Comput. 27(4), 299–309 (2011)

    Article  CAS  Google Scholar 

  32. W. Qiang, O. Sourina, N. Minh Khoa, EEG-based “Serious” games design for medical applications, in Proceedings of the International Conference on Cyberworlds, 2010

    Google Scholar 

  33. O. Sourina, Y. Liu, Q. Wang, M. Nguyen, EEG-based personalized digital experience, in Universal Access in Human–Computer Interaction. Users Diversity, ed. by C. Stephanidis (Springer, Berlin, 2006), pp. 591–599

    Google Scholar 

  34. B.H. Cho, J.M. Lee, J.H. Ku, D.P. Jang, J.S. Kim, I.Y. Kim, J.H. Lee, S.I. Kim, Attention enhancement system using virtual reality and EEG biofeedback, in Proceedings of IEEE Virtual Reality, 2002

    Google Scholar 

  35. J.D. Bayliss, D.H. Ballard, A virtual reality testbed for brain–computer interface research. IEEE Trans. Rehabil. Eng. 8(2), 188–190 (2000)

    Article  CAS  PubMed  Google Scholar 

  36. M. Linden, T. Habib, V. Radojevic, A controlled study of the effects of EEG biofeedback on cognition and behavior of children with attention deficit disorder and learning disabilities. Appl. Psychophys. Biof. 21(1), 35–49 (1996)

    CAS  Google Scholar 

  37. M. Arns, S. de Ridder, U. Strehl, M. Breteler, A. Coenen, Efficacy of neurofeedback treatment in ADHD: the effects on inattention, impulsivity and hyperactivity: a meta-analysis. Clin. EEG Neurosci. 40(3), 180–9 (2009)

    Article  PubMed  Google Scholar 

  38. E. Saxby, E.G. Peniston, Alpha–theta brainwave neurofeedback training: an effective treatment for male and female alcoholics with depressive symptoms. J. Clin. Psychol. 51(5), 685–693 (1995)

    Article  CAS  PubMed  Google Scholar 

  39. G. Prasad, P. Herman, D. Coyle, S. McDonough, J. Crosbie, Applying a brain–computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study. J. Neuro Eng. Rehabil. 7(1), 60 (2010)

    Article  Google Scholar 

  40. B. Scolnick, Effects of electroencephalogram biofeedback with Asperger’s syndrome. Int. J. Rehabil. Res. 28(2), 159–163 (2005)

    Article  PubMed  Google Scholar 

  41. M.E.J. Kouijzer, H.T. van Schie, J.M.H. de Moor, B.J.L. Gerrits, J.K. Buitelaar, Neurofeedback treatment in autism. Preliminary findings in behavioral, cognitive, and neurophysiological functioning. Res. Autism Spect. Dis. 4(3), 386–399 (2010)

    Article  Google Scholar 

  42. T.M. Sokhadze, R.L. Cannon, D.L. Trudeau, EEG biofeedback as a treatment for substance use disorders: review, rating of efficacy and recommendations for further research. J. Neurother. 12(1), 5–43 (2008)

    Article  Google Scholar 

  43. N. Weiskopf, K. Mathiak, S.W. Bock, F. Scharnowski, R. Veit, W. Grodd, R. Goebel, N. Birbaumer, Principles of a brain–computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI). IEEE Trans. Biomed. Eng. 51(6), 966–970 (2004)

    Article  PubMed  Google Scholar 

  44. N. Weiskopf, F. Scharnowski, R. Veit, R. Goebel, N. Birbaumer, K. Mathiak, Self-regulation of local brain activity using real-time functional magnetic resonance imaging (fMRI). J. Physiol.-Paris 98(4–6), 357–373 (2004)

    Article  PubMed  Google Scholar 

  45. V. Zotev, F. Krueger, R. Phillips, R.P. Alvarez, W.K. Simmons, P. Bellgowan, W.C. Drevets, J. Bodurka, Self-regulation of amygdala activation using real-time fMRI neurofeedback. PLoS ONE 6(9), e24522 (2011)

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  46. S. Haller, N. Birbaumer, R. Veit, Real-time fMRI feedback training may improve chronic tinnitus. Eur. Radiol. 20(3), 696–703 (2010)

    Article  PubMed  Google Scholar 

  47. R. Sitaram, A. Caria, R. Veit, T. Gaber, G. Rota, A. Kuebler, N. Birbaumer, fMRI brain–computer interface: a tool for neuroscientific research and treatment. Intell. Neurosci. 2007, 1–10 (2007)

    Article  Google Scholar 

  48. S.-S. Yoo, T. Fairneny, N.-K. Chen, S.-E. Choo, L.P. Panych, H. Park, S.-Y. Lee, F.A. Jolesz, Brain–computer interface using fMRI: spatial navigation by thoughts. NeuroReport 15(10), 1591–1595 (2004)

    Article  PubMed  Google Scholar 

  49. G. Kramer (ed.), Auditory Display: Sonification, Audification, and Auditory Interface. Proceedings, Santa Fe Institute Studies in the Sciences of Complexity (Westview Press, Reading, 1994)

    Google Scholar 

  50. T. Hermann, A. Hunt, J. Neuhoff (eds.), The Sonification Handbook, 1st edn. (Logos Publishing House, Berlin, 2011)

    Google Scholar 

  51. J. Keller, Sonification for Beginners [updated: 25 January 2010; cited: 25 March 2012]. Available from: http://cse.ssl.berkeley.edu/stereo_solarwind/sounds_programs.html

  52. H. Berger, Über Das Elektrenkephalogramm Des Menschen. Arch. Psychiatr. Nervenkr. 87, 527–570 (1929)

    Article  Google Scholar 

  53. H. Berger, On the electroencephalogram of man. Electroencephalogr. Clin. Neurophysiol. 28, 133 (1969)

    Google Scholar 

  54. E.F.M. Wijdicks, The diagnosis of brain death. N. Engl. J. Med. 344(16), 1215–1221 (2001)

    Article  CAS  PubMed  Google Scholar 

  55. D.J. Powner, G.H. Fromm, The electroencephalogram in the determination of brain death. New Engl. J. Med. 300(9), 502 (1979)

    CAS  PubMed  Google Scholar 

  56. E. Gütling, A. Gonser, H.-G. Imhof, T. Landis, EEG reactivity in the prognosis of severe head injury. Neurology 45(5), 915–918 (1995)

    Article  PubMed  Google Scholar 

  57. R.W. Thatcher, C. Biver, R. McAlaster, M. Camacho, A. Salazar, Biophysical linkage between MRI and EEG amplitude in closed head injury. NeuroImage 7(4), 352–367 (1998)

    Article  CAS  PubMed  Google Scholar 

  58. K.G. Jordan, Emergency EEG and continuous EEG monitoring in acute ischemic stroke. J. Clin. Neurophysiol. 21(5), 341–352 (2004)

    PubMed  Google Scholar 

  59. R.A. Jackel, R.N. Harner, Computed EEG topography in acute stroke. Clin. Neurophysiol. 19(3), 185–197 (1989)

    Article  CAS  Google Scholar 

  60. P.M. Vespa, M.R. Nuwer, C. Juhász, M. Alexander, V. Nenov, N. Martin, D.P. Becker, Early detection of vasospasm after acute subarachnoid hemorrhage using continuous EEG ICU monitoring. Electroencephalogr. Clin. Neurophysiol. 103(6), 607–615 (1997)

    Article  CAS  PubMed  Google Scholar 

  61. J. Claassen, S.A. Mayer, L.J. Hirsch, Continuous EEG monitoring in patients with subarachnoid hemorrhage. J. Clin. Neurophysiol. 22(2), 92–98 (2005)

    Article  PubMed  Google Scholar 

  62. J. Dauwels, F. Vialatte, A. Cichocki, Diagnosis of Alzheimer’s disease from EEG signals: where are we standing? Curr. Alzheimer Res. 7, 487–505 (2010)

    Article  CAS  PubMed  Google Scholar 

  63. J. Dauwels, F. Vialatte, T. Musha, A. Cichocki, A comparative study of synchrony measures for the early diagnosis of Alzheimer’s disease based on EEG. NeuroImage 49, 668–693 (2010)

    Article  CAS  PubMed  Google Scholar 

  64. J. Dauwels, K. Srinivasan, R. Reddy, T. Musha, F. Vialatte, C. Latchoumane, J. Jeong, A. Cichocki, Slowing and loss of complexity in Alzheimer’s EEG: two sides of the same coin? Int. J. Alzheimer's Dis. 2011, 539621 (2011)

    Google Scholar 

  65. F.-B. Vialatte, J. Solé-Casals, M. Maurice, C. Latchoumane, N. Hudson, S. Wimalaratna, J. Jeong, A. Cichocki, Improving the quality of EEG data in patients with Alzheimer’s disease using ICA, in Advances in Neuro-Information Processing. Lecture Notes in Computer Science, vol. 5507, 2009, pp. 979–986

    Google Scholar 

  66. F.N. Karameh, M.A. Dahleh, Automated classification of EEG signals in brain tumor diagnostics, in Proceedings of American Control Conference, 2000

    Google Scholar 

  67. R. Silipo, G. Deco, H. Bartsch, Brain tumor classification based on EEG hidden dynamics. Intell. Data Anal. 3(4), 287–306 (1999)

    Article  Google Scholar 

  68. R. Benca, W. Obermeyer, C. Larson, B. Yun, I. Dolski, K. Kleist, S. Weber, R. Davidson, EEG alpha power and alpha power asymmetry in sleep and wakefulness. Psychophysiology 36(04), 430–436 (1999)

    Article  CAS  PubMed  Google Scholar 

  69. H. Merica, R. Blois, J.M. Gaillard, Spectral characteristics of sleep EEG in chronic insomnia. Eur. J. Neurosci. 10(5), 1826–1834 (1998)

    Article  CAS  PubMed  Google Scholar 

  70. N. Kannathal, M.L. Choo, U.R. Acharya, P.K. Sadasivan, Entropies for detection of epilepsy in EEG. Comput. Methods Prog. Biomed. 80(3), 187–194 (2005)

    Article  CAS  Google Scholar 

  71. K.K. Jerger, T.I. Netoff, J.T. Francis, T. Sauer, L. Pecora, S.L. Weinstein, S.J. Schiff, Early seizure detection. J. Clin. Neurophysiol. 18(3), 259–268 (2001)

    Article  CAS  PubMed  Google Scholar 

  72. P.J. Marshall, Y. Bar-Haim, N.A. Fox, Development of the EEG from 5 months to 4 years of age. Clin. Neurophysiol. 113(8), 1199–1208 (2002)

    Article  PubMed  Google Scholar 

  73. A. Meyer-Lindenberg, The evolution of complexity in human brain development: an EEG study. Electroencephalogr. Clin. Neurophysiol. 99(5), 405–411 (1996)

    Article  CAS  PubMed  Google Scholar 

  74. X.S. Zhang, R.J. Roy, E.W. Jensen, EEG complexity as a measure of depth of anesthesia for patients. IEEE Trans. Biomed. Eng. 48(12), 1424–1433 (2001)

    Article  CAS  PubMed  Google Scholar 

  75. M.C. Salinsky, B.S. Oken, L. Morehead, Intraindividual analysis of antiepileptic drug effects on EEG background rhythms. Electroencephalogr. Clin. Neurophysiol. 90(3), 186–193 (1994)

    Article  CAS  PubMed  Google Scholar 

  76. J. Bruhn, H. Röpcke, A. Hoeft, Approximate entropy as an electroencephalographic measure of anesthetic drug effect during desflurane anesthesia. Anesthesiology 92(3), 715–726 (2000)

    Article  CAS  PubMed  Google Scholar 

  77. F.B. Vialatte, J. Dauwels, T. Musha, A. Cichocki, Audio representations of multi-channel EEG: a new tool for diagnosis of brain disorders, Am. J. Neurodegener. Dis. 1(3), 292–304 (2012)

    Google Scholar 

  78. F. Vialatte, A. Cichocki, Sparse bump sonification: a new tool for multichannel EEG diagnosis of mental disorders, application to the detection of the early stage of Alzheimer’s disease, in Neural Information Processing (ICONIP 2006), ed. by I. King, J. Wang, L.-W. Chan, D. Wang. Lecture Notes in Computer Science (LNCS), vol. 4234 (Springer, Heidelberg, 2006), pp. 92–101

    Google Scholar 

  79. T. Rutkowski, F. Vialatte, A. Cichocki, D. Mandic, A. Barros, Auditory feedback for brain computer interface management—an EEG data sonification approach, in Knowledge-Based Intelligent Information and Engineering Systems. Lecture Notes in Computer Science (LNCS), vol. 4253 (Springer, Berlin, 2006), pp. 1232–1239

    Google Scholar 

  80. T. Hinterberger, Orchestral sonification of brain signals and its applications to brain–computer interfaces and performing arts, in Proceedings of the Second International Workshop on Interactive Sonification, York, 2007

    Google Scholar 

  81. P. Nunez, R. Srinivasan (eds.), Electric Fields of the Brain (Oxford University Press, New York, 2006)

    Google Scholar 

  82. F. Vialatte, A. Cichocki, G. Dreyfus, T. Musha, T.M. Rutkowski, R. Gervais, Blind source separation and sparse bump modelling of time frequency representation of EEG signals: new tools for early detection of Alzheimer’s disease, in IEEE Workshop on Machine Learning for Signal Processing, 2005

    Google Scholar 

  83. A. Lucier, Statement On: Music for Solo Performer. Biofeedback and the Arts: Results of Early Experiments (Aesthetic Research Centre of Canada, Vancouver, 1967)

    Google Scholar 

  84. D. Rosenboom, Method for producing sounds or light flashes with alpha brain waves for artistic purposes. Leonardo 5(2), 141–145 (1972)

    Article  Google Scholar 

  85. R. Teitelbaum, In Tune: Some Early Experiments in Biofeedback Music (1966–1974) (Aesthetic Research Center of Canada Publications, Vancouver, 1976)

    Google Scholar 

  86. D. Rosenboom, The performing brain. Comput. Music. J. 14(1), 48–66 (1990)

    Article  Google Scholar 

  87. E.R. Miranda, K. Sharman, K.A. Kilborn, A. Duncan, On harnessing the electroencephalogram for the musical braincap. Comput. Music. J. 27(2), 80–102 (2003)

    Article  Google Scholar 

  88. G. Baier, T. Hermann, The sonification of rhythms in human electroencephalogram, in Proceedings of ICAD 2004, Sydney, 2004

    Google Scholar 

  89. NeuroFocus [updated: 2012; cited: 23 April 2012]. Available from: http://www.neurofocus.com/

  90. Biopac [updated: 2012; cited: 15 March 2012]. Available from: http://www.biopac.com/researchApplications.asp?Aid=23&AF=437&Level=3

  91. EmotivSystems, Emotiv—Brain Computer Interface Technology [updated: 21 April 2012; cited: 23 April 2012]. Available from: http://emotiv.com

  92. Imec [updated: 8 February 2011; cited: 23 April 2012]. Available from: http://www2.imec.be/be_en/press/imec-news/imecEEGMDMWest.html

  93. Neurobelt, Medical Computer Systems [updated: 30 March 2011; cited: 20 April 2012]. Available from: http://www.mks.ru/eng/Products/EEG/Neurobelt/

  94. BCI2000, General-Purpose System for Brain Computer Interface [updated: 2010; cited: 2012]. Available from: http://www.bci2000.org/BCI2000/Home.html

  95. J. Harrison, pyPortMidi [updated: 15 March 2010; cited: 23 April 2011]. Available from: http://alumni.media.mit.edu/~harrison/code.html

  96. Numpy [updated: 2011; cited: 21 February 2011]. Available from: http://numpy.scipy.org/

  97. SynthFont, SynthFont and other tools for Midi and SoundFonts [updated: 22 March 2012; cited: 23 April 2012]. Available from: http://www.synthfont.com/

  98. MIDI-OX [updated: 29 January 2011; cited: 18 February 2011]. Available from: http://www.midiox.com/

  99. G. Baier, T. Hermann, U. Stephani, Event-based sonification of EEG rhythms in real time. Clin. Neurophysiol. 118(6), 1377–1386 (2007)

    Article  PubMed  Google Scholar 

  100. M. Hanke, Y.O. Halchenko, P.B. Sederberg, S.J. Hanson, J.V. Haxby, S. Pollmann, PyMVPA: a python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics 7, 37–53 (2009)

    Article  PubMed Central  PubMed  Google Scholar 

  101. C. Dede, Immersive interfaces for engagement and learning. Science 323(5910), 66–69 (2009)

    Article  CAS  PubMed  Google Scholar 

  102. R. Kizony, N. Katz, P.L. Weiss, Adapting an immersive virtual reality system for rehabilitation. J. Vis. Comput. Animat. 14(5), 261–268 (2003)

    Article  Google Scholar 

  103. A. Gorini, A. Gaggioli, C. Vigna, G. Riva, A second life for eHealth: prospects for the use of 3-D virtual worlds in clinical psychology. J. Med. Internet Res. 10(3), e21 (2008)

    Article  PubMed Central  PubMed  Google Scholar 

  104. H. Hoffman, T. Richards, A. Bills, T. Oosstrom, J. Magula, E. Seibel, S. Sharar, Using fMRI to study the neural correlates of virtual reality analgesia. CNS Spectr. 11, 45–51 (2006)

    PubMed  Google Scholar 

  105. R. Leeb, D. Friedman, G.R. Müller-Putz, R. Scherer, M. Slater, G. Pfurtscheller, Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic. Comput. Intell. Neurosci. 2007, 79642 (2007). doi: 10.1155/2007/79642

  106. R. Leeb, F. Lee, C. Keinrath, R. Scherer, H. Bischof, G. Pfurtscheller, Brain–computer communication: motivation, aim, and impact of exploring a virtual apartment. IEEE Trans. Neural Syst. Rehabil. Eng. 15(4), 473–482 (2007)

    Article  PubMed  Google Scholar 

  107. T. Ros, M.A.M. Munneke, D. Ruge, J.H. Gruzelier, J.C. Rothwell, Endogenous control of waking brain rhythms induces neuroplasticity in humans. Eur. J. Neurosci. 31(4), 770–778 (2010)

    Article  PubMed  Google Scholar 

  108. J. Gruzelier, A. Inoue, R. Smart, A. Steed, T. Steffert, Acting performance and flow state enhanced with sensory-motor rhythm neurofeedback comparing ecologically valid immersive VR and training screen scenarios. Neurosci. Lett. 480(2), 112–116 (2010)

    Article  CAS  PubMed  Google Scholar 

  109. B. Fry, C. Reas, Processing [updated: 15 May 2011; cited: 2 May 2012]. Available from: http://processing.org/

  110. G. McMillan, Socket Programming HOWTO [updated: 3 January 2011; cited: 2 May 2012]. Available from: http://docs.python.org/howto/sockets.html

  111. B. Fry, C. Reas, Server [updated: 30 August 2010; cited: 2 May 2012]. Available from: http://processing.org/reference/libraries/net/Server.html

  112. B. Glueck, C. Stroebel, Biofeedback and meditation in the treatment of psychiatric illnesses. Compr. Psychiatry 16(4), 302–321 (1975)

    Article  Google Scholar 

  113. R.C. deCharms, F. Maeda, G.H. Glover, D. Ludlow, J.M. Pauly, D. Soneji, J.D.E. Gabrieli, S.C. Mackey, Control over brain activation and pain learned by using real-time functional MRI. Proc. Natl. Acad. Sci. U. S. A. 102(51), 18626–18631 (2005)

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  114. P. Marti, M. Bacigalupo, L. Giusti, C. Mennecozzi, T. Shibata, Socially assistive robotics in the treatment of behavioural and psychological symptoms of dementia, in Proceedings of The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob 2006), 2006

    Google Scholar 

Download references

Acknowledgment

Mohamed Elgendi and Justin Dauwels would like to thank the Institute for Media Innovation (IMI) at Nanyang Technological University (NTU) for partially supporting this project (Grant M58B40020).

Author information

Authors and Affiliations

  1. University of Alberta, Edmonton Alberta, T6G 2E1, Canada

    Mohamed Elgendi

  2. School of Electrical Engineering, Nanyang Technological University, Singapore, Singapore

    Justin Dauwels, Yosmar Putra, Niu ZePing & Amrish Nair

  3. Temasek Laboratories, National University of Singapore, Singapore, Singapore

    Brice Rebsamen

  4. University of Wisconsin-Madison, Madison, WI, 53706, United States

    Rohit Shukla

  5. Universidad Nacional Autonoma de Mexico, Mexico, Mexico

    Jorge Gamez

  6. Hwa Chong Institution, Singapore, Singapore

    Bangying Ho

  7. Drexel University, Philadelphia, PA, USA

    Niteesh Prasad

  8. BITS-Pilani, Goa Campus, Zuarinagar, India

    Dhruv Aggarwal

  9. Belarusian State University of Informatics and Radioelectronics, Minsk, Belarus

    Vasilisa Mishuhina

  10. ESPCI ParisTech, Paris, France

    Francois Vialatte

  11. School of Art, Design and Media, Nanyang Technological University, Singapore, Singapore

    Martin Constable

  12. Lab. ABSP, RIKEN Brain Science Institute, Wako-Shi, Japan

    Andrzej Cichocki

  13. Center for Neural Science, Korea Institute of Science and Technology, Seoul, South Korea

    Charles Latchoumane

  14. Korea Advanced Institute of Science and Technology, Daejeon, South Korea

    Jaesung Jeong

  15. Institute for Media Innovation, Nanyang Technological University, Singapore, Singapore

    Daniel Thalmann & Nadia Magnenat-Thalmann

Authors
  1. Mohamed Elgendi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Justin Dauwels
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Brice Rebsamen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rohit Shukla
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yosmar Putra
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jorge Gamez
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Niu ZePing
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Bangying Ho
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Niteesh Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Dhruv Aggarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Amrish Nair
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Vasilisa Mishuhina
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Francois Vialatte
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Martin Constable
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Andrzej Cichocki
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Charles Latchoumane
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Jaesung Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Daniel Thalmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Nadia Magnenat-Thalmann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Justin Dauwels .

Editor information

Editors and Affiliations

  1. National University of Singapore, Singapore, Singapore

    Zhi Yang

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this chapter

Cite this chapter

Elgendi, M. et al. (2014). From Auditory and Visual to Immersive Neurofeedback: Application to Diagnosis of Alzheimer’s Disease. In: Yang, Z. (eds) Neural Computation, Neural Devices, and Neural Prosthesis. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8151-5_4

Download citation

Publish with us

Policies and ethics

Search

Navigation