Use of ANNs as classifiers for selective attention brain-computer interfaces

  • Authors:

  • Miguel Ángel López;Héctor Pomares;Miguel Damas;Eduardo Madrid;Alberto Prieto;Francisco Pelayo;Eva María De la Plaza Hernández

  • Affiliations:

  • Department of Computer Architecture and Computer Technology, University of Granada;Department of Computer Architecture and Computer Technology, University of Granada;Department of Computer Architecture and Computer Technology, University of Granada;Department of Experimental Psychology and University of Granada;Department of Computer Architecture and Computer Technology, University of Granada;Department of Computer Architecture and Computer Technology, University of Granada;Department of Experimental Psychology and University of Granada

  • Venue:
  • IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
  • Year:
  • 2007

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Abstract

Selective attention to visual-spatial stimuli causes decrements of power in alpha band and increments in beta. For steady-state visual evoked potentials (SSVEP) selective attention affects electroencephalogram (EEG) recordings, modulating the power in the range 8-27 Hz. The same behaviour can be seen for auditory stimuli as well, although for auditory steady-state response (ASSR), it is not fully confirmed yet. The design of selective attention based brain-computer interfaces (BCIs) has two major advantages: First, no much training is needed. Second, if properly designed, a steady-state response corresponding to spectral peaks can be elicited, easy to filter and classify. In this paper we study the behaviour of ANNs as classifiers for a selective attention to auditory stimuli based BCI system.