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EEG Based Biometric Framework for Automatic Identity Verification

  • Ramaswamy Palaniappan
  • Danilo P. Mandic
Article

Abstract

The energy of brain potentials evoked during processing of visual stimuli is considered as a new biometric. In particular, we propose several advances in the feature extraction and classification stages. This is achieved by performing spatial data/sensor fusion, whereby the component relevance is investigated by selecting maximum informative (EEG) electrodes (channels) selected by Davies–Bouldin index. For convenience and ease of cognitive processing, in the experiments, simple black and white drawings of common objects are used as visual stimuli. In the classification stage, the Elman neural network is employed to classify the generated EEG energy features. Simulations are conducted by using the hold-out classification strategy on an ensemble of 1,600 raw EEG signals, and 35 maximum informative channels achieved the maximum recognition rate of 98.56 ± 1.87%. Overall, this study indicates the enormous potential of the EEG biometrics, especially due to its robustness against fraud.

Keywords

biometric Davies–Bouldin index electroencephalogram identity identification neural network 

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of EssexColchesterUK
  2. 2.Department of Electrical and Electronic EngineeringImperial College LondonLondonUK

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