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EEG Based Biometric Framework for Automatic Identity Verification

Ramaswamy PalaniappanContact Information and Danilo P. MandicContact Information

(1)  Department of Computer Science, University of Essex, Colchester, Essex, CO4 3SQ, UK
(2)  Department of Electrical and Electronic Engineering, Imperial College London, London, UK

Received: 24 April 2006  Revised: 26 October 2006  Accepted: 2 April 2007  Published online: 28 June 2007

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


Contact Information Ramaswamy Palaniappan (Corresponding author)
Email: rpalan@essex.ac.uk

Contact Information Danilo P. Mandic
Email: d.mandic@imperial.ac.uk

Ramaswamy Palaniappan   received his first degree and M.Eng.Sc. degree in Electrical Engineering and Ph.D. degree in Microelectronics/Biomedical Engineering in 1997, 1999 and 2002, respectively from University of Malaya, Kuala Lumpur, Malaysia. He is currently a lecturer with the Department of Computing and Electronic Systems (formerly known as Computer Science), University of Essex, United Kingdom where he is the coordinator of Biosignal Analysis Group. Prior to this, he was the Associate Dean and Senior Lecturer at Multimedia University, Malaysia and Research Fellow in the Biomedical Engineering Research Centre-University of Washington Alliance, Nanyang Technological University, Singapore. He has been a referee to the Industry Grant Scheme managed by Ministry of Science, Technology & Environment, Malaysia. He helped to set-up the Biomedical Engineering Department in University Malaya, Malaysia and founded and chaired the Bioinformatics division in Centre for Bioinformatics and Biometrics in Multimedia University, Malaysia. His current research interests include biological signal processing, brain-computer interfaces, biometrics, artificial neural networks, genetic algorithms, and image processing. To date, he has published over 100 papers in peer-reviewed journals, book chapters, and conference proceedings. Dr. Palaniappan is a member of the Institute of Electrical and Electronics Engineers, IEEE Engineering in Medicine and Biology Society, Institution of Engineering and Technology, and Biomedical Engineering Society. He also serves as Editorial Board member for International Journal of Computer Science and Network Security, and International Journal of Information Processing. His pioneering work on using brain signals as biometrics has received international recognition.
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Dr. Mandic   received the Ph.D. degree in nonlinear adaptive signal processing in 1999 from Imperial College, London, London, U.K. He is now a reader with the Department of Electrical and Electronic Engineering, Imperial College London, London, U.K. He has previously taught at the Universities of East Anglia, Norwich, Norfolk, U.K., and Banja Luka, Bosnia Herzegovina. He has written over 150 publications on a variety of aspects of signal processing and a research monograph on recurrent neural networks. He has been a Guest Professor at the Catholic University Leuven, Leuven, Belgium and Tokyo University of Agriculture and Technology (TUAT), and Frontier Researcher at the Brain Science Institute RIKEN, Tokyo, Japan. Dr. Mandic has been a member of the IEEE Signal Processing Society Technical Committee on Machine Learning for Signal Processing, Associate Editor for IEEE Transactions on Circuits and Systems II, and Associate Editor for International Journal of Mathematical Modeling and Algorithms. He has won awards for his papers and for the products coming from his collaboration with industry.
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