Palaniappan, Ramaswamy, Mandic, D.P. (2007) Biometrics from brain electrical activity: A machine learning approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29 (4). pp. 738-742. ISSN 0162-8828. (doi:10.1109/TPAMI.2007.1013) (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) (KAR id:70725)
| 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. | |
| Official URL: http://dx.doi.org/10.1109/TPAMI.2007.1013 |
Abstract
The potential of brain electrical activity generated as a response to a visual stimulus is examined in the context of the identification of individuals. Specifically, a framework for the Visual Evoked Potential (VEP)-based biometrics is established, whereby energy features of the gamma band within VEP signals were of particular interest. A rigorous analysis is conducted which unifies and extends results from our previous studies, in particular, with respect to 1) increased bandwidth, 2) spatial averaging, 3) more robust power spectrum features, and 4) improved classification accuracy. Simulation results on a large group of subject support the analysis. © 2007 IEEE.
| Item Type: | Article |
|---|---|
| DOI/Identification number: | 10.1109/TPAMI.2007.1013 |
| Additional information: | Unmapped bibliographic data: LA - English [Field not mapped to EPrints] J2 - IEEE Trans Pattern Anal Mach Intell [Field not mapped to EPrints] C2 - 17299228 [Field not mapped to EPrints] AD - Department of Computer Science, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, United Kingdom [Field not mapped to EPrints] AD - Department of Electrical and Electronic Engineering, Imperial College of Science, Technology and Medicine, Exhibition Road, London SW7 2 BT, United Kingdom [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Article [Field not mapped to EPrints] |
| Uncontrolled keywords: | Biometrics, EEG gamma band, Elman neural network, k-nearest neighbors, MUSIC, Visual evoked potential, Bioelectric phenomena, Brain, Computer simulation, Electroencephalography, Learning systems, Elman neural networks, Gamma band, Visual evoked potential, Biometrics, algorithm, article, artificial intelligence, automated pattern recognition, biometry, brain, brain mapping, electroencephalography, evaluation, evoked response, human, methodology, physiology, Algorithms, Artificial Intelligence, Biometry, Brain, Brain Mapping, Electroencephalography, Evoked Potentials, Humans, Pattern Recognition, Automated |
| Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
| Depositing User: | Palaniappan Ramaswamy |
| Date Deposited: | 15 Dec 2018 11:46 UTC |
| Last Modified: | 05 Nov 2024 12:33 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/70725 (The current URI for this page, for reference purposes) |
University of Kent Author Information
Palaniappan, Ramaswamy.
| Creator's ORCID: |
 https://orcid.org/0000-0001-5296-8396
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