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Reseach Article

Feature Extraction of EEG Signal using Wavelet Transform

by Ashwini Nakate, P.D. Bahirgonde
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 124 - Number 2
Year of Publication: 2015
Authors: Ashwini Nakate, P.D. Bahirgonde
10.5120/ijca2015905370

Ashwini Nakate, P.D. Bahirgonde . Feature Extraction of EEG Signal using Wavelet Transform. International Journal of Computer Applications. 124, 2 ( August 2015), 21-24. DOI=10.5120/ijca2015905370

@article{ 10.5120/ijca2015905370,
author = { Ashwini Nakate, P.D. Bahirgonde },
title = { Feature Extraction of EEG Signal using Wavelet Transform },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 124 },
number = { 2 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 21-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume124/number2/22077-2015905370/ },
doi = { 10.5120/ijca2015905370 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:14:46.059671+05:30
%A Ashwini Nakate
%A P.D. Bahirgonde
%T Feature Extraction of EEG Signal using Wavelet Transform
%J International Journal of Computer Applications
%@ 0975-8887
%V 124
%N 2
%P 21-24
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

EEG signal analysis is such an important thing for disease analysis and brain–computer analysis. Using Electroencephalography (EEG) monitoring the state of the user’s brain functioning and treatment for any psychological disorder, where the difficulty in learning and comprehending the arithmetic exists and it could allow for analysis disease the user to train the corresponding brain. In this paper, we proposed a method for EEG signal processing includes signal de-noising, segmentation of de-noise signal using PCM and signal segments feature extraction done using wavelet as an alternative to the commonly used discrete Fourier transform (DFT).These feature classified using support vector machine classifier, Using the Matlab software proposed method accompanied.

References
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Index Terms

Computer Science
Information Sciences

Keywords

EEG (Electroencephalography) segmentation PCM DWT SVM.