Abstract
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.
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References
Access Economics Pty Limited (2009) Future Sight Loss UK: Economic impact of partial sight and blindness in the UK adult population
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):539–550
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):41–56
Blauert J (1997) Spatial hearing: the psychophysics of human sound localization. MIT Press, Cambridge
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 74:937–946
Burns R (1929) Blumlein and the birth of stereo. IEE Review: 269–273
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 1–7
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, Warwich
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 13:461–467
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 122:2003–2009
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 46:617–625
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):49–58
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 121:516–523
Hartmann WM (1983) Localization of sound in rooms. J Acoust Soc Am 74:1380–1391
Hinterberger T, Baier G (2005) Poser: parametric orchestral sonification of EEG in real-time for the self-regulation of brain states. IEEE Multimed 12:70–79
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 154:521–526
Hinterberger T (2007) Orchestral sonification of brain signals and its application to brain-computer interfaces and performing arts. Workshop Interact Sonification, In
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 49:567–577
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 4185–4188
Höhne J, Schreuder M, Blankertz B, Tangermann M (2011) A novel 9-class auditory ERP paradigm driving a predictive text entry system. Front Neurosci 5:1–10
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 457–457
Müller-Putz GR, Scherer R, Brunner C, Leeb R, Pfurtscheller G (2008) Better than random? A closer look on BCI results. Int J Bioelectromagn 10:52–55
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):1–13
Nijboer F, Furdea A, Gunst I, Mellinger J, McFarland DJ, Birbaumer N, Kübler A (2008) An auditory brain-computer interface (BCI). J Neurosci Methods 167:43–50
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 119:1909–1916
Ohki M, Kanayama R, Nakamura T, Okuyama T, Kimura Y, Koike Y (1994) Ocular abnormalities in amyotrophic lateral sclerosis. Acta Oto-laryngologica Suppl 511:138–142
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):316–325
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 19:206–218
Pulkki V (2002) Compensating displacement of amplitude-panned virtual sources. In: AES 22nd International Conference, pp 186–195
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 197–202
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 23:186–193
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 105–108
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):e9813
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 Neuroscience
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 6691:337–344
Vidaurre C, Blankertz B (2010) Towards a cure for BCI illiteracy. Brain Topogr 23(2):194–198
Acknowledgments
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.
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McCreadie, K.A., Coyle, D.H. & Prasad, G. Sensorimotor learning with stereo auditory feedback for a brain–computer interface. Med Biol Eng Comput 51, 285–293 (2013). https://doi.org/10.1007/s11517-012-0992-7
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DOI: https://doi.org/10.1007/s11517-012-0992-7