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Advances in EEG-Based Biometry

  • António Ferreira
  • Carlos Almeida
  • Pétia Georgieva
  • Ana Tomé
  • Filipe Silva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6112)

Abstract

This paper is focused on proving the concept that the EEG signals collected during a perception or mental task can be used for discrimination of individuals. The viability of the EEG-based person identification was successfully tested for a data base of 13 persons. Among various classifiers tested, Support Vector Machine (SVM) with Radial Basis Function (RBF) exhibits the best performance. The problem of static classification that does not take into account the temporal nature of the EEG sequence was considered by an empirical post classifier procedure. The algorithm proposed has an effect of introducing a memory into the classifier without increasing its complexity. Control of a classified access into restricted areas security systems, health disorder identification in medicine, gaining more understanding of the cognitive human brain processes in neuroscience are some of the potential applications of EEG-based biometry.

Keywords

Classification support-vector machine biometry electroencephalogram (EEG) 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • António Ferreira
    • 1
  • Carlos Almeida
    • 1
  • Pétia Georgieva
    • 1
  • Ana Tomé
    • 1
  • Filipe Silva
    • 1
  1. 1.Department of Electronics, Telecommunications and Informatics/IEETAUniversity of AveiroAveiroPortugal

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