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

  • Mohamed Elgendi
  • Justin Dauwels
  • Brice Rebsamen
  • Rohit Shukla
  • Yosmar Putra
  • Jorge Gamez
  • Niu ZePing
  • Bangying Ho
  • Niteesh Prasad
  • Dhruv Aggarwal
  • Amrish Nair
  • Vasilisa Mishuhina
  • Francois Vialatte
  • Martin Constable
  • Andrzej Cichocki
  • Charles Latchoumane
  • Jaesung Jeong
  • Daniel Thalmann
  • Nadia Magnenat-Thalmann

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.

Keywords

Alzheimer Disease Sonification System Sound Sequence Bird Sound Brain Topographic Mapping 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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).

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Mohamed Elgendi
    • 1
  • Justin Dauwels
    • 2
  • Brice Rebsamen
    • 3
  • Rohit Shukla
    • 4
  • Yosmar Putra
    • 2
  • Jorge Gamez
    • 5
  • Niu ZePing
    • 2
  • Bangying Ho
    • 6
  • Niteesh Prasad
    • 7
  • Dhruv Aggarwal
    • 8
  • Amrish Nair
    • 2
  • Vasilisa Mishuhina
    • 9
  • Francois Vialatte
    • 10
  • Martin Constable
    • 11
  • Andrzej Cichocki
    • 12
  • Charles Latchoumane
    • 13
  • Jaesung Jeong
    • 14
  • Daniel Thalmann
    • 15
  • Nadia Magnenat-Thalmann
    • 15
  1. 1.University of AlbertaEdmonton AlbertaCanada
  2. 2.School of Electrical EngineeringNanyang Technological UniversitySingaporeSingapore
  3. 3.Temasek LaboratoriesNational University of SingaporeSingaporeSingapore
  4. 4.University of Wisconsin-MadisonMadisonUnited States
  5. 5.Universidad Nacional Autonoma de MexicoMexicoMexico
  6. 6.Hwa Chong InstitutionSingaporeSingapore
  7. 7.Drexel UniversityPhiladelphiaUSA
  8. 8.BITS-PilaniZuarinagarIndia
  9. 9.Belarusian State University of Informatics and RadioelectronicsMinskBelarus
  10. 10.ESPCI ParisTechParisFrance
  11. 11.School of Art, Design and MediaNanyang Technological UniversitySingaporeSingapore
  12. 12.Lab. ABSP, RIKEN Brain Science InstituteWako-ShiJapan
  13. 13.Center for Neural ScienceKorea Institute of Science and TechnologySeoulSouth Korea
  14. 14.Korea Advanced Institute of Science and TechnologyDaejeonSouth Korea
  15. 15.Institute for Media Innovation, Nanyang Technological UniversitySingaporeSingapore

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