A Minimal Channel Set for Individual Identification with EEG Biometric Using Genetic Algorithm

Ravi, K.V.R., Palaniappan, Ramaswamy (2007) A Minimal Channel Set for Individual Identification with EEG Biometric Using Genetic Algorithm. In: Proceeding of ICCIMA 2007. 2. pp. 328-333. IEEE ISBN 978-0-7695-3050-5. (doi:10.1109/ICCIMA.2007.19) (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:70716)

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/ICCIMA.2007.19

Abstract

In this paper, we explore the use of genetic algorithm (GA) to select a minimum number of channels that identifies individuals based on brain signals i.e. electroencephalogram (EEG). The fusion of GA with linear discriminant classifier shows that the identification performance of EEG signals from 40 subjects does not degrade when using 23 selected channels as compared to all the available 61 channels as studied previously. As the channel identification method by GA is general, it could be used in any feature reduction application.

Item Type:	Conference or workshop item (Proceeding)
DOI/Identification number:	10.1109/ICCIMA.2007.19
Additional information:	Unmapped bibliographic data: C7 - 4426716 [EPrints field already has value set] LA - English [Field not mapped to EPrints] J2 - Proc. Int. Conf. Comput. Intell. Multimedia Appl. ICCIMA [Field not mapped to EPrints] AD - School of Information and Communications Technology, Republic Polytechnic, 9 Woodlands Ave 9, 738964, Singapore, Singapore [Field not mapped to EPrints] AD - Biosignal Analysis Group, Dept. of Computing and Electronic Systems, University of Essex, Wivenhoe Park, Colchester, CO 4 3SQ, United Kingdom [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Conference Paper [Field not mapped to EPrints] C3 - Proceedings - International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2007 [Field not mapped to EPrints]
Uncontrolled keywords:	Feature extraction, Genetic algorithms, Signal processing, Channel identification, Channel set, Feature reduction, Electroencephalography
Divisions:	Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User:	Palaniappan Ramaswamy
Date Deposited:	15 Dec 2018 19:36 UTC
Last Modified:	05 Nov 2024 12:33 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/70716 (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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