The Role of Personalization and Multiple EEG and Sound Features Selection in Real Time Sonification for Neurofeedback

S. Mealla, A. Oliveira, X. Marimon, T. Steffert, S. Jordà, A. Väljamäe

2014

Abstract

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.

References

  1. Allanson, J. and Fairclough, S. (2004). A research agenda for physiological computing. Interacting with Computers, 16(5):857-878.
  2. Bradley, M. M. and Lang, P. J. (1994). Measuring emotion: The self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 25(1):49 - 59.
  3. De Campo, A., Hoeldrich, R., Eckel, G., and Wallisch, A. (2007). New Sonification Tools For Eeg Data Screening And Monitoring. In Proceedings of the 13th International Conference on Auditory Display, volume 67(2009)90, pages 536-542.
  4. Duvinage, M., Castermans, T., Petieau, M., Hoellinger, T., Cheron, G., and Dutoit, T. (2013). Performance of the emotiv epoc headset for p300-based applications. Biomed Eng Online, 12:56.
  5. Egner, T., Strawson, E., and Gruzelier, J. (2002). Eeg signature and phenomenology of alpha/theta neurofeedback training versus mock feedback. Applied Psychophysiology and Biofeedback, 27(4):261-270.
  6. Emotiv (2013). Emotiv epoc. http://www.emotiv.com.
  7. Gruzelier, J. (2009). A theory of alpha/theta neurofeedback, creative performance enhancement, long distance functional connectivity and psychological integration. Cognitive Processing, 10(1):101-109.
  8. Guttman, S. E., Gilroy, L. A., and Blake, R. (2005). Hearing what the eyes see: Auditory encoding of visual temporal sequences. Psychological Science, 16(3):228-235.
  9. Hermann, T., Meinicke, P., and Bekel, H. (2002). Sonifications for EEG data analysis. In Proceedings of the International Conference on Auditory Display (ICAD 2002), pages 3-7, Kyoto.
  10. Hjorth, B. (1970). Eeg analysis based on time domain properties. Electroencephalography and clinical neurophysiology, 29(3):306-310.
  11. Khamis, H., Mohamed, A., Simpson, S., and McEwan, A. (2012). Detection of temporal lobe seizures and identification of lateralisation from audified EEG. Clinical Neurophysiology, 123(9):1714-20.
  12. Kropotov, J. (2010). Quantitative EEG, event-related potentials and neurotherapy. Elsevier.
  13. Liang, S.-F., Chen, Y.-C., Wang, Y.-L., Chen, P.-T., Yang, C.-H., and Chiueh, H. (2013). A hierarchical approach for online temporal lobe seizure detection in long-term intracranial eeg recordings. Journal of neural engineering, 10(4):045004.
  14. Mealla, S., Väljamäe, A., Bosi, M., and Jordà, S. (2011). Listening to your brain: Implicit interaction in collaborative music performances. In Proc. of NIME, pages 149-154.
  15. Mullen, T., Luther, M., Way, K., and Jansch, A. (2011). Minding the (Transatlantic) Gap: An Internet-Enabled Acoustic Brain-Computer Music Interface followed throughout the next decade by a number of artists. In Proc. NIME'11.
  16. Rodríguez, A., Rey, B., and Alcan˜ iz, M. (2013). Validation of a low-cost eeg device for mood induction studies. Studies in health technology and informatics, 191:43- 47.
  17. Shinn-Cunningham, B. G. (2008). Object-based auditory and visual attention. Trends Cogn Sci, 12(5):182-186.
  18. Tajadura-Jimenez, A., Valjamae, A., and Vastfjall, D. (2008). Self-representation in mediated environments: the experience of emotions modulated by auditoryvibrotactile heartbeat. Cyberpsychology and Behavior, 11(1):33-38.
  19. Väljamäe, A., Mealla, S., Steffert, T., Holland, S., Marimon, X., Benitez, R., and et al. (2013). A Review Of Real-time EEG Sonification Research. In The 19th International Conference on Auditory Display (ICAD2013), Lodz, Poland.
  20. Vastfjall, D. (2003). The subjective sense of presence, emotion recognition, and experienced emotions in auditory virtual environments. Cyberpsycholy and Behavior, 6(2):181-188.
  21. Wright, M. (2005). Open sound control: an enabling technology for musical networking. Organised Sound, 10(03):193-200.
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Paper Citation


in Harvard Style

Mealla S., Oliveira A., Marimon X., Steffert T., Jordà S. and Väljamäe A. (2014). The Role of Personalization and Multiple EEG and Sound Features Selection in Real Time Sonification for Neurofeedback . In Proceedings of the International Conference on Physiological Computing Systems - Volume 1: PhyCS, ISBN 978-989-758-006-2, pages 323-330. DOI: 10.5220/0004727203230330


in Bibtex Style

@conference{phycs14,
author={S. Mealla and A. Oliveira and X. Marimon and T. Steffert and S. Jordà and A. Väljamäe},
title={The Role of Personalization and Multiple EEG and Sound Features Selection in Real Time Sonification for Neurofeedback},
booktitle={Proceedings of the International Conference on Physiological Computing Systems - Volume 1: PhyCS,},
year={2014},
pages={323-330},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004727203230330},
isbn={978-989-758-006-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Physiological Computing Systems - Volume 1: PhyCS,
TI - The Role of Personalization and Multiple EEG and Sound Features Selection in Real Time Sonification for Neurofeedback
SN - 978-989-758-006-2
AU - Mealla S.
AU - Oliveira A.
AU - Marimon X.
AU - Steffert T.
AU - Jordà S.
AU - Väljamäe A.
PY - 2014
SP - 323
EP - 330
DO - 10.5220/0004727203230330