Medical & Biological Engineering & Computing

, Volume 51, Issue 3, pp 285–293 | Cite as

Sensorimotor learning with stereo auditory feedback for a brain–computer interface

  • Karl A. McCreadieEmail author
  • Damien H. Coyle
  • Girijesh Prasad
Original Article


Motor imagery can be used to modulate sensorimotor rhythms (SMR) enabling detection of voltage fluctuations on the surface of the scalp using electroencephalographic electrodes. Feedback is essential in learning to modulate SMR for non-muscular communication using a brain–computer interface (BCI). A BCI not reliant upon the visual modality not only releases the visual channel for other uses but also offers an attractive means of communication for the physically impaired who are also blind or vision impaired. This study demonstrates the feasibility of replacing the traditional visual feedback modality with stereo auditory feedback. Results from a pilot study were used to select the most appropriate sounds for auditory feedback based on three options: broadband noise and two anechoic instrument samples. Subsequently, an SMR BCI was used to examine the effect on sensorimotor learning with broadband noise utilising a modified stereophonic presentation method. Twenty participants split into equal groups took part in ten sessions. The visual group performed best initially but did not improve over time whilst the auditory group improved as the study progressed. The results demonstrate the feasibility of using stereophonic auditory feedback with broadband noise as opposed to other auditory feedback presentation methods and sounds which are less intuitive.


Brain-computer interface Sensorimotor rhythm Auditory feedback Stereophonic sound EEG 



This research is supported by the Intelligent Systems Research Centre (ISRC), Department for Employment and Learning Northern Ireland (DELNI) and the UK Engineering and Physical Sciences Research Council (EPSRC) (project no. EP/H012958/1). All participants are also kindly thanked for their time and effort.

Supplementary material

11517_2012_992_MOESM1_ESM.doc (48 kb)
Supplementary material (DOC 40 kb)


  1. 1.
    Access Economics Pty Limited (2009) Future Sight Loss UK: Economic impact of partial sight and blindness in the UK adult populationGoogle Scholar
  2. 2.
    Blankertz B, Dornhege G, Krauledat M, Müller KR, Curio G (2007) The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects. NeuroImage 37(2):539–550PubMedCrossRefGoogle Scholar
  3. 3.
    Blankertz B, Tomioka R, Lemm S, Kawanabe M, Muller KR (2008) Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process Mag 25(1):41–56CrossRefGoogle Scholar
  4. 4.
    Blauert J (1997) Spatial hearing: the psychophysics of human sound localization. MIT Press, CambridgeGoogle Scholar
  5. 5.
    Brungart DS, Kordik AJ, Simpson BD, McKinley RL (2003) Auditory localization in the horizontal plane with single and double hearing protection. Aviat Space Environ Med 74:937–946PubMedGoogle Scholar
  6. 6.
    Burns R (1929) Blumlein and the birth of stereo. IEE Review: 269–273Google Scholar
  7. 7.
    Coyle D, Garcia J, Satti AR, Mcginnity TM (2011) EEG-based continuous control of a game using a 3 channel motor imagery BCI. In: 2011 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), pp 1–7Google Scholar
  8. 8.
    Coyle DH, Satti AR, Stow J, McCreadie K, Carroll A, McEelligott, J (2011) Operating a brain computer interface: able bodied versus physically impaired performance. In: Recent Advances in Assistive Technology and Engineering Conference, WarwichGoogle Scholar
  9. 9.
    Coyle D, Prasad G, McGinnity TM (2005) A time-series prediction approach for feature extraction in a brain-computer interface. IEEE Trans Neural Systems Rehabil Eng 13:461–467CrossRefGoogle Scholar
  10. 10.
    Friedrich EVC, Scherer R, Sonnleitner K, Neuper C (2011) Impact of auditory distraction on user performance in a brain-computer interface driven by different mental tasks. Clin Neurophysiol 122:2003–2009PubMedGoogle Scholar
  11. 11.
    Furdea A, Halder S, Krusienski DJ, Bross D, Nijboer F, Birbaumer N, Kübler A (2009) An auditory oddball (P300) spelling system for brain-computer interfaces. Psychophysiology 46:617–625PubMedCrossRefGoogle Scholar
  12. 12.
    Guger C, Schlögl A, Neuper C, Walterspacher D, Strein T, Pfurtscheller G (2001) Rapid Prototyping of an EEG-Based Brain-Computer Interface. IEEE Trans Neural Sys Rehabil Eng 9(1):49–58CrossRefGoogle Scholar
  13. 13.
    Halder S, Rea M, Andreoni R, Nijboer F, Hammer EM, Kleih SC, Birbaumer N, Kübler A (2010) An auditory oddball brain–computer interface for binary choices. Clin Neurophysiols 121:516–523CrossRefGoogle Scholar
  14. 14.
    Hartmann WM (1983) Localization of sound in rooms. J Acoust Soc Am 74:1380–1391PubMedCrossRefGoogle Scholar
  15. 15.
    Hinterberger T, Baier G (2005) Poser: parametric orchestral sonification of EEG in real-time for the self-regulation of brain states. IEEE Multimed 12:70–79CrossRefGoogle Scholar
  16. 16.
    Hinterberger T, Neumann N, Pham M, Kübler A, Grether A, Hofmayer N, Wilhelm B, Flor H, Birbaumer N (2004) A multimodal brain-based feedback and communication system. Exp Brain Res 154:521–526PubMedCrossRefGoogle Scholar
  17. 17.
    Hinterberger T (2007) Orchestral sonification of brain signals and its application to brain-computer interfaces and performing arts. Workshop Interact Sonification, InGoogle Scholar
  18. 18.
    Horki P, Solis-Escalante T, Neuper C, Müller-Putz G (2011) Combined motor imagery and SSVEP based BCI control of a 2 DoF artificial upper limb. Med Biol Eng Comput 49:567–577PubMedCrossRefGoogle Scholar
  19. 19.
    Höhne J, Schreuder M, Blankertz B, Tangermann M (2010) Two-dimensional auditory p300 speller with predictive text system. In: Annual International Conference IEEE Engineering Medicine and Biology Society, pp 4185–4188Google Scholar
  20. 20.
    Höhne J, Schreuder M, Blankertz B, Tangermann M (2011) A novel 9-class auditory ERP paradigm driving a predictive text entry system. Front Neurosci 5:1–10CrossRefGoogle Scholar
  21. 21.
    Lv J, Liu M (2008) Common spatial pattern and particle swarm optimization for channel selection in BCI. In: 2008 3rd International Conference on Innovative Computing Information and Control, pp 457–457Google Scholar
  22. 22.
    Müller-Putz GR, Scherer R, Brunner C, Leeb R, Pfurtscheller G (2008) Better than random? A closer look on BCI results. Int J Bioelectromagn 10:52–55Google Scholar
  23. 23.
    Nijboer F, Birbaumer N, Kübler A (2010) The influence of psychological state and motivation on brain-computer interface performance in patients with amyotrophic lateral sclerosis—a longitudinal study. Front Neurosci 4(55):1–13Google Scholar
  24. 24.
    Nijboer F, Furdea A, Gunst I, Mellinger J, McFarland DJ, Birbaumer N, Kübler A (2008) An auditory brain-computer interface (BCI). J Neurosci Methods 167:43–50PubMedCrossRefGoogle Scholar
  25. 25.
    Nijboer F, Sellers EW, Mellinger J, Jordan MA, Matuz T, Furdea A, Halder S, Mochty U, Krusienski DJ, Vaughan TM, Wolpaw JR, Birbaumer N, Kübler A (2008) A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. Clin Neurophysiol 119:1909–1916PubMedCrossRefGoogle Scholar
  26. 26.
    Ohki M, Kanayama R, Nakamura T, Okuyama T, Kimura Y, Koike Y (1994) Ocular abnormalities in amyotrophic lateral sclerosis. Acta Oto-laryngologica Suppl 511:138–142CrossRefGoogle Scholar
  27. 27.
    Pfurtscheller G, Neuper C, Schlögl A, Lugger K (1998) Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. IEEE Trans Rehabil Eng 6(3):316–325PubMedCrossRefGoogle Scholar
  28. 28.
    Pham M, Hinterberger T, Neumann N, Kübler A, Hofmayer N, Grether A, Wilhelm B, Vatine JJ, Birbaumer N (2005) An auditory brain-computer interface based on the self-regulation of slow cortical potentials. Neurorehabilitation Neural Repair 19:206–218PubMedCrossRefGoogle Scholar
  29. 29.
    Pulkki V (2002) Compensating displacement of amplitude-panned virtual sources. In: AES 22nd International Conference, pp 186–195Google Scholar
  30. 30.
    Rutkowski TM, Tanaka T, Zhao Q, Cichocki A (2010) Spatial auditory BCI/BMI paradigm-multichannel EMD approach to brain responses estimation. In: APSIPA Annual Summit and Conference, pp 197–202Google Scholar
  31. 31.
    Sannelli C, Dickhaus T, Halder S, Hammer E-M, Müller K-R, Blankertz B (2010) On optimal channel configurations for SMR-based brain-computer interfaces. Brain Topogr 23:186–193PubMedCrossRefGoogle Scholar
  32. 32.
    Satti A, Guan C, Coyle D, Prasad G (2010) A covariate shift minimisation method to alleviate non-stationarity effects for an adaptive brain-computer interface. In: 20th International Conference on Pattern Recognition, pp 105–108Google Scholar
  33. 33.
    Schreuder M, Blankertz B, Tangermann M (2010) A new auditory multi-class brain-computer interface paradigm: spatial hearing as an informative cue. PLoS ONE 5(4):e9813PubMedCrossRefGoogle Scholar
  34. 34.
    Stow J, Coyle D, Carroll A, Satti A, McElligott J (2011) Achievable brain computer communication through short intensive motor imagery training despite long term spinal cord injury. In: Annual IICN Registrar’s Prize in NeuroscienceGoogle Scholar
  35. 35.
    Velasco-Álvarez F, Ron-Angevin R, da Silva-Sauer L, Sancha-Ros S, Blanca-Mena M, Cabestany J, Rojas I, Joya G (2011) Audio-cued SMR brain-computer interface to drive a virtual wheelchair. Adv Comput Intell 6691:337–344Google Scholar
  36. 36.
    Vidaurre C, Blankertz B (2010) Towards a cure for BCI illiteracy. Brain Topogr 23(2):194–198PubMedCrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2012

Authors and Affiliations

  • Karl A. McCreadie
    • 1
    Email author
  • Damien H. Coyle
    • 1
  • Girijesh Prasad
    • 1
  1. 1.School of Computing and Intelligent SystemsUniversity of Ulster, MageeDerry/LondonderryNorthern Ireland

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