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Multi-Channel Noise Reduced Visual Evoked Potential Analysis

Palaniappan, Ramaswamy, Raveendran, P., Nishida, S. (2003) Multi-Channel Noise Reduced Visual Evoked Potential Analysis. IEEJ Transactions on Electronics, Information and Systems, 123 (10). pp. 1721-1727. ISSN 0385-4221. (doi:10.1541/ieejeiss.123.1721) (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/10.1541/ieejeiss.123.1721

Abstract

In this paper, Principal Component Analysis (PCA) is used to reduce noise from multi-channel Visual Evoked Potential (VEP) signals. PCA is applied to reduce noise from multi-channel VEP signals because VEP signals are more correlated from one channel to another as compared to noise during visual perception. Emulated VEP signals contaminated with noise are used to show the noise reduction ability of PCA. These noise reduced VEP signals are analysed in the gamma spectral band to classify alcoholics and non-alcoholics with a Fuzzy ARTMAP (FA) neural network. A zero phase Butterworth digital filter is used to extract gamma band power in spectral range of 30 to 50 Hz from these noise reduced VEP signals. The results using 800 VEP signals give an average FA classification of 92.50% with the application of PCA and 83.33% without the application of PCA. © 2003, The Institute of Electrical Engineers of Japan. All rights reserved.

Item Type: Article
DOI/Identification number: 10.1541/ieejeiss.123.1721
Additional information: Unmapped bibliographic data: LA - English [Field not mapped to EPrints] J2 - IEEJ Trans. Electron. Inf. Syst. [Field not mapped to EPrints] AD - Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia, Japan [Field not mapped to EPrints] AD - Dept, of Electrical Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia, Japan [Field not mapped to EPrints] AD - Dept. of Systems and Human Science, Graduate School of Engineering Science, Osaka University, Machikaneyama-chou 1-3, Toyonaka, Osaka 560-8531, Japan [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Article [Field not mapped to EPrints]
Uncontrolled keywords: Alcoholics,Fuzzy ARTMAP. Gamma band, Object recognition, Principal component analysis, Visual stimulus
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 15 Dec 2018 17:42 UTC
Last Modified: 30 May 2019 08:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70758 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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