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Evaluation of Identity Information Loss in EEG-Based Biometric Systems

  • Meriem Romaissa BoubakeurEmail author
  • Guoyin Wang
  • Ke Liu
  • Karima Benatchba
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11976)

Abstract

Recently, electroencephalogram (EEG) has been used as a biometric modality. An EEG-based biometric system allows an automatic recognition of people based on their EEG signals. The quantity and quality of identity information extracted from EEG determine the performance of the EEG-based biometric system. In this paper, we evaluate the loss in identity information through different signal segmentation scenarios using Autoregressive model and K-Nearest Neighbor classifier. Our objective is to find some criteria linked to data segmentation allowing to reduce as far as possible the simulated loss of identity information. Experiments were conducted on EEG publicly available datasets collected in resting state for both opened and closed eyes. Results show that overlapped segmentation with longer segments’ length stands the best to the simulated loss favoring larger percentages of overlap.

Keywords

EEG Biometrics Person identification Segmentation 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Meriem Romaissa Boubakeur
    • 1
    Email author
  • Guoyin Wang
    • 1
  • Ke Liu
    • 1
  • Karima Benatchba
    • 2
  1. 1.Chongqing Key Laboratory of Computational IntelligenceChongqing University of Posts and TelecommunicationsChongqingChina
  2. 2.Laboratoire des Méthodes de Conception de SystèmesEcole nationale Supérieure d’InformatiqueAlgerAlgeria

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