Use of Kohonen Maps as Feature Selector for Selective Attention Brain-Computer Interfaces

  • Authors:

  • Miguel Angel Lopez;Hector Pomares;Miguel Damas;Alberto Prieto;Eva Maria Plaza Hernandez

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

  • Venue:
  • IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
  • 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 braincomputer 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 Kohonen Maps as feature selector for a selective attention to auditory stimuli based BCI system.