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Improving visual evoked potential feature classification for person recognition using PCA and normalization

Palaniappan, Ramaswamy, Ravi, K.V.R. (2006) Improving visual evoked potential feature classification for person recognition using PCA and normalization. Pattern Recognition Letters, 27 (7). pp. 726-733. ISSN 0167-8655. (doi:10.1016/j.patrec.2005.10.020) (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)

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Official URL
https://doi.org/10.1016/j.patrec.2005.10.020

Abstract

In earlier papers, it was shown that recognizing persons using their brain patterns evoked during visual stimulus is possible. In this paper, several modifications are proposed to improve the recognition accuracy. In the method, gamma band spectral power (GBSP) features were computed from the visual evoked potential (VEP) signals recorded from 61 electrodes while subjects perceived a picture. Two methods were used to improve the classification rate. First, principal component analysis (PCA) was used to reduce the noise and background electroencephalogram (EEG) effects from the VEP signals. Second, the GBSP of each channel was normalized by the total GBSP from all the channels. Three classifiers were used: simplified fuzzy ARTMAP (SFA), linear discriminant (LD) and k-nearest neighbor (kNN). The experimental results using 800 VEP signals from 20 subjects with leave-one-out cross-validation strategy showed that PCA improves the classification performance for all the classifiers with normalization giving improved results in certain cases. The best classification performance of 96.50% obtained using the improved method shows that brain signals have suitable biometric properties that could be further exploited.

Item Type: Article
DOI/Identification number: 10.1016/j.patrec.2005.10.020
Additional information: Unmapped bibliographic data: LA - English [Field not mapped to EPrints] J2 - Pattern Recogn. Lett. [Field not mapped to EPrints] AD - Department of Computer Science, University of Essex, Wivenhoe Park, Colchester, Essex CO4 3SQ, United Kingdom [Field not mapped to EPrints] AD - Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Article [Field not mapped to EPrints]
Uncontrolled keywords: Biometrics, Electroencephalogram, Linear discriminant, Nearest neighbor, Person identification, Principal component analysis, Simplified fuzzy ARTMAP, Visual evoked potential, Brain, Electroencephalography, Fuzzy sets, Principal component analysis, Signal processing, Biometrics, Gamma band spectral power (GBSP), Linear discriminant (LD), Person identification, Feature extraction
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 15 Dec 2018 17:22 UTC
Last Modified: 30 May 2019 08:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70733 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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