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Vision-Free Brain-Computer Interface using auditory selective attention: evaluation of training effect

  • Ana Paula SouzaEmail author
  • Leonardo Bonato Felix
  • Antonio Mauricio Miranda de Sá
  • Eduardo M. A. M. Mendes
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 57)

Abstract

Evoked Potentials (EPs) recorded in electroencephalograms (EEGs) are widely applied in Brain-Computer Interface (BCI). However, many of these interfaces can cause fatigue because of the length of the sessions and the training or eye control required in the case of visual BCIs. The solution to this problem lies in the use of BCIs with auditory evoked potential using auditory steady state response (ASSR) – which can be modulated in a selective attention condition. Consequently, this study analyzed the training effect of a binary auditory BCI using selective attention and stimulation with amplitude-modulated (AM) tones. To assess the performance of this BCI, the hit rate achieved by 20 volunteers was verified over a period of four weeks. The results showed that the hit rate distribution over the four weeks presented similar values. Further, statistical analysis demonstrated that there was no statistical difference between these rates at a \(5\%\) significance level. Thus, a volunteer can use the auditory BCI with selective attention in a interval of one week, without any improvement in performance.

Keywords

BCI Evoked Potential ASSR Selective Attention training effect 

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References

  1. 1.
    Hill N J, Scholkopf B. An online Brain–Computer Interface based onshifting Attention to concurrent streams of Auditory Stimuli Journal ofNeural Engineering. 2012;9:026011:13Google Scholar
  2. 2.
    Lopez-Gordo M A, Fernandes E, Romero S, Pelayo F, Prieto A. An AuditoryBrain-Computer Interface Evoked by Natural Speech Journal of NeuralEngineering. 2012;9(3):026011Google Scholar
  3. 3.
    Brumberg J S, Guenther F H, Kennedy P R. An Auditory Output Brain–ComputerInterface for Speech Communication Brain-Computer Interface ResearchSpringer Briefs in Electrical and Computer Engineering. 2013:7-14Google Scholar
  4. 4.
    Yin E, Zhou Z, Jiang J, Chen F, Liu Y, Hu D. A Novel Hybrid BCI Speller basedon the incorporation of SSVEP into the P300 paradigm Journal of NeuralEngineering. 2013;10: 026012:9Google Scholar
  5. 5.
    Chiappa K H. Evoked Potentials in Clinical Medicine. New York: Raven Press2 ed. 1997Google Scholar
  6. 6.
    Kim D W, Hwang H J, Lim J H, Lee Y H, Jung K Y, Im C H. Classification ofselective attention to auditory stimuli: Toward vision-free brain computerinterfacing Journal of Neuroscience Methods. 2011;197:180-185Google Scholar
  7. 7.
    Felix L B, Ranaudo F S, Neto A D’affonseca, Sá A M F L M. A spatialapproach of magnitude-squared coherence applied to selective attentiondetection Journal of Neuroscience Methods. 2014;229:28-32Google Scholar
  8. 8.
    Henry M J, Obleser J. Frequency modulation entrains slow neural oscillations and optimizes human listening behavior in Proc Natl Acad Sci USA;109(USA):20095–20100 2012Google Scholar
  9. 9.
    Henry M J, Obleser J. Dissociable Neural Response Signatures for Slow Amplitudeand Frequency Modulation in Human Auditory Cortex PLoSONE. 2013;8:e78758Google Scholar
  10. 10.
    Bidet-Caulet A, Fischer C, Besle J, Aguera P E, Giard M H, BertrandO. Effects of Selective Attention on the Electrophysiological Representationof Concurrent Sounds in the Human Auditory Cortex TheJournal of Neuroscience. 2007;27:9252–9261Google Scholar
  11. 11.
    Schreuder M, Blankertz B, Tangermann M. A New Auditory Multi-ClassBrain-Computer Interface Paradigm: Spatial Hearing as an Informative CuePLoS ONE. 2010;5:9813Google Scholar
  12. 12.
    Ranaudo F S. Atenção Seletiva Auditiva usando Potenciais Evocados em Regime Permanente e Coerência Espacial in Dissertação de Mestrado. Programa de Pós-graduação em Engenharia Biomédica, COPPE – Universidade Federal do Rio de Janeiro(Rio de Janeiro)2012Google Scholar
  13. 13.
    Caria A, Weber C, Brötz D, et al. Chronicstrokerecovery aftercombined BCI trainingand physiotherapy: A case reportGoogle Scholar
  14. 14.
    Guo M, Xu G, Wang L, Wang J. Research on Auditory BCI Based on Wavelet Transform in Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS)(IEEE International Conference) 2012Google Scholar
  15. 15.
    Halder S, Hammer E M, Kleih S C, et al. Prediction of Auditory and Visual P300 Brain-Computer Interface Aptitude PLoSONE. 2013;8:53513Google Scholar
  16. 16.
    Felix L B, Moraes J E, Sá A M F L Miranda DE. Avoiding spectral leakage in objective detection of auditory steady-state evoked responses in the inferior colliculus of rat using coherence Journal of Neuroscience Methods. 2005;144:249–255Google Scholar
  17. 17.
    Muller N, SchleeW, Hartmann T. Top-down modulation of the auditory steady-state response in a task-switch paradigm Frontiers in Human Neuroscience. 2009;3:1–9Google Scholar
  18. 18.
    Infantosi A F, Melges D B, Tierra-Criollo C J. Use of magnitudesquaredcoherence to identify the maximum driving response band of the somatosensory evoked potential Brazilian Journal of Medical and Biological Research. 2006;39:1593–1603Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ana Paula Souza
    • 1
    • 2
    Email author
  • Leonardo Bonato Felix
    • 3
  • Antonio Mauricio Miranda de Sá
    • 4
  • Eduardo M. A. M. Mendes
    • 1
  1. 1.Programa de Pós-Graduação em Engenharia ElétricaUniversidade Federal de Minas GeraisBelo HorizonteBrazil
  2. 2.Instituto de Ciências Exatas e TecnológicasUniversidade Federal de Viçosa - Campus FlorestalFlorestalBrazil
  3. 3.Departamento de Engenharia ElétricaUniversidade Federal de ViçosaViçosaBrazil
  4. 4.Programa de Engenharia Biomédica/COPPEUniversidade Federal do Rio de JaneiroRio de JaneiroBrazil

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