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Reseach Article

EEG Signal with Feature Extraction using SVM and ICA Classifiers

by Chunchu Rambabu, B. Rama Murthy
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 85 - Number 3
Year of Publication: 2014
Authors: Chunchu Rambabu, B. Rama Murthy

Chunchu Rambabu, B. Rama Murthy . EEG Signal with Feature Extraction using SVM and ICA Classifiers. International Journal of Computer Applications. 85, 3 ( January 2014), 1-7. DOI=10.5120/14818-3046

@article{ 10.5120/14818-3046,
author = { Chunchu Rambabu, B. Rama Murthy },
title = { EEG Signal with Feature Extraction using SVM and ICA Classifiers },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 85 },
number = { 3 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { },
doi = { 10.5120/14818-3046 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:01:30.027641+05:30
%A Chunchu Rambabu
%A B. Rama Murthy
%T EEG Signal with Feature Extraction using SVM and ICA Classifiers
%J International Journal of Computer Applications
%@ 0975-8887
%V 85
%N 3
%P 1-7
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

Identifying artifacts in EEG data produced by the neurons in brain is an important task in EEG signal processingresearch. Theseartifacts are corrected before further analyzing. In this work, fast fixed point algorithm for Independent Component Analysis (ICA) is used for removing artifacts in EEG signals and principal component analysis (PCA) tool is used for reducing high dimensional data and spatial redundancy. Support vector machine (SVM) tool is used for pattern recognition of EEG signals and the extracted parameters are used to impart cognitive interpretation ability towards autonomous system design.

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Index Terms

Computer Science
Information Sciences


EEG signals Fast ICA PCA SVM and Hardware Architecture