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EEG Based Biometric Framework for Automatic Identity Verification

Palaniappan, Ramaswamy, Mandic, D.P. (2007) EEG Based Biometric Framework for Automatic Identity Verification. Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology, 49 (2). pp. 243-250. ISSN 1939-8018. (doi:10.1007/s11265-007-0078-1) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1007/s11265-007-0078-1

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 98.56â??±â??1.87%. Overall, this study indicates the enormous potential of the EEG biometrics, especially due to its robustness against fraud. © 2007 Springer Science+Business Media, LLC.

Item Type: Article
DOI/Identification number: 10.1007/s11265-007-0078-1
Additional information: Unmapped bibliographic data: LA - English [Field not mapped to EPrints] J2 - J VLSI Signal Process Syst Signal Image Video Technol [Field not mapped to EPrints] AD - Department of Computer Science, University of Essex, Colchester, Essex CO4 3SQ, United Kingdom [Field not mapped to EPrints] AD - Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Article [Field not mapped to EPrints]
Uncontrolled keywords: Biometric, Davies-Bouldin index, Electroencephalogram, Identity identification, Neural network, Classification (of information), Electroencephalography, Feature extraction, Robustness (control systems), Sensor data fusion, Davies-Bouldin index, Identity identification, White drawings, Biometrics
Divisions: Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 12 Dec 2018 22:35 UTC
Last Modified: 30 May 2019 08:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70724 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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