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The role of personalization and multiple EEG and sound features selection in real time sonification for neurofeedback
Music Technology Group (MTG), Department of Information and Communication Technologies (DTIC), Universitat Pompeu Fabra, Barcelona, Spain.
Music Technology Group (MTG), Department of Information and Communication Technologies (DTIC), Universitat Pompeu Fabra, Barcelona, Spain.
Automatic Control Department, LASSIE Lab, Universitat Politecnica de Catalunya, Barcelona, Spain.
Centre for Research in Computing, Open University, Milton Keynes, Buckinghamshire, United Kingdom.
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2014 (English)In: PhyCS 2014 - Proceedings of the International Conference on Physiological Computing Systems, SciTePress , 2014, 323-330 p.Conference paper, Published paper (Refereed)
Abstract [en]

The field of physiology-based interaction and monitoring is developing at a fast pace. Emerging applications like fatigue monitoring often use sound to convey complex dynamics of biological signals and to provide an alternative, non-visual information channel. Most Physiology-to-Sound mappings in such auditory displays do not allow customization by the end-users. We designed a new sonification system that can be used for extracting, processing and displaying Electroencephalography data (EEG) with different sonification strategies. The system was validated with four user groups performing alpha/theta neurofeedback training (a/t) for relaxation that varied in feedback personalization (Personalized/Fixed) and a number of sonified EEG features (Single/Multiple). The groups with personalized feedback performed significantly better in their training than fixed mappings groups, as shown by both subjective ratings and physiological indices. Additionally, the higher number of sonified EEG features resulted in deeper relaxation than when training with single feature feedback. Our results demonstrate the importance of adaptation and personaliziation of EEG sonification according to particular applications, in our case, to a/t neurofeedback. Our experimental approach shows how user performance can be used for validating different sonification strategies. Copyright © 2014 SCITEPRESS - Science and Technology Publications. All rights reserved.

Place, publisher, year, edition, pages
SciTePress , 2014. 323-330 p.
Keyword [en]
Alpha/theta neurofeedback; EEG; Physiological computing; Pure data; Real time; Sonification; Sound
National Category
Basic Medicine
Identifiers
URN: urn:nbn:se:liu:diva-116790DOI: 10.5220/0004727203230330Scopus ID: 2-s2.0-84907309873ISBN: 9789897580062 (print)OAI: oai:DiVA.org:liu-116790DiVA: diva2:801595
Conference
International Conference on Physiological Computing Systems, PhyCS 2014
Available from: 2015-04-09 Created: 2015-04-02 Last updated: 2015-04-09

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Väljamäe, Aleksander

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf