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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hill N J, Scholkopf B. An online Brain–Computer Interface based onshifting Attention to concurrent streams of Auditory Stimuli Journal ofNeural Engineering. 2012;9:026011:13
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):026011
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. 2013:7-14
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: 026012:9
Chiappa K H. Evoked Potentials in Clinical Medicine. New York: Raven Press2 ed. 1997
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;197:180-185
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;229:28-32
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 2012
Henry M J, Obleser J. Dissociable Neural Response Signatures for Slow Amplitudeand Frequency Modulation in Human Auditory Cortex PLoSONE. 2013;8:e78758
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;27:9252–9261
Schreuder M, Blankertz B, Tangermann M. A New Auditory Multi-ClassBrain-Computer Interface Paradigm: Spatial Hearing as an Informative CuePLoS ONE. 2010;5:9813
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)2012
Caria A, Weber C, Brötz D, et al. Chronicstrokerecovery aftercombined BCI trainingand physiotherapy: A case report
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) 2012
Halder S, Hammer E M, Kleih S C, et al. Prediction of Auditory and Visual P300 Brain-Computer Interface Aptitude PLoSONE. 2013;8:53513
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;144:249–255
Muller N, SchleeW, Hartmann T. Top-down modulation of the auditory steady-state response in a task-switch paradigm Frontiers in Human Neuroscience. 2009;3:1–9
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;39:1593–1603
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Souza, A.P., Felix, L.B., de Sá, A.M.M., Mendes, E.M.A.M. (2016). Vision-Free Brain-Computer Interface using auditory selective attention: evaluation of training effect. In: Kyriacou, E., Christofides, S., Pattichis, C. (eds) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-32703-7_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-32703-7_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32701-3
Online ISBN: 978-3-319-32703-7
eBook Packages: EngineeringEngineering (R0)