Welcome!
To use the personalized features of this site, please log in or register.
If you have forgotten your username or password, we can help.
|
 |
EEG Based Biometric Framework for Automatic Identity Verification
| |
|
EEG Based Biometric Framework for Automatic Identity Verification
Ramaswamy Palaniappan1 and Danilo P. Mandic2 
| (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
Fulltext Preview (Small, Large)
 References secured to subscribers.
|
|
|
|
|
|