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Statistical Characterization of Steady-State Visual Evoked Potentials and Their Use in Brain–Computer Interfaces

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Abstract

Steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs) use the spectral power of the potentials for classification as they can be voluntarily enhanced or diminished by the subject by means of selective attention. The features traditionally extracted from the EEG and used for BCIs have been characterized as a normal distribution, although some studies have shown recently that this normal distribution is not the most appropriate for SSVEPs. In this paper we attempt to characterize the power of SSVEPs as a random variable that follows Rayleigh and exponential distributions when the stimulus is attended and ignored, respectively. BCIs based on SSVEPs can improve the transfer-bit and successful-classification rates if this new model is used instead of the traditional one based on the normal distribution.

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Authors and Affiliations

  1. Department of Signal Theory, Telematics and Communications, University of Granada, Granada, Spain

    Miguel A. Lopez

  2. Department of Architecture and Technology of Computers, University of Granada, Granada, Spain

    Francisco Pelayo & Alberto Prieto

  3. Faculty of Psychology, University of Granada, Granada, Spain

    Eduardo Madrid

Authors
  1. Miguel A. Lopez
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  2. Francisco Pelayo
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  3. Eduardo Madrid
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  4. Alberto Prieto
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Corresponding author

Correspondence to Miguel A. Lopez.

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Lopez, M.A., Pelayo, F., Madrid, E. et al. Statistical Characterization of Steady-State Visual Evoked Potentials and Their Use in Brain–Computer Interfaces. Neural Process Lett 29, 179–187 (2009). https://doi.org/10.1007/s11063-009-9102-8

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  • DOI: https://doi.org/10.1007/s11063-009-9102-8

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