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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
  1. M. Rajya Lakshmi, Dr. T. V. Prasad, Dr. V. Chandra Prakash “Survey on EEG Signal Processing Methods” International Journal of Advanced Research in Computer Science and Software Engineering Volume 4, Issue 1, January 2014.
  2. Miguel Rivera, Laura Salas “Monitoring of Micro-sleep and Sleepiness for the Drivers Using EEG Signal” School of Innovation, Design and Engineering (IDT) Malardalen University Vasteras, Sweden.
  3. Jon Shlens “A Tutorial on Principal Component Analysis (Derivation, Discussion and Singular Value Decomposition)” 25 March 2003
  4. Mark Richardson “Principal Component Analysis” May 2009
  5. Jon Shlens “A TUTORIAL ON PRINCIPAL COMPONENT ANALYSIS”
  6. Dipti Upadhyay “Classification of EEG Signals under Different Mental Tasks Using Wavelet Transform and Neural Network with One Step Secant Algorithm” International Journal of Scientific Engineering and Technology, Volume 2 Issue 4, pp : 256-259
  7. Pravin A. Kharat, Sanjay V. Dudul “Daubechies Wavelet Neural Network Classifier for the Diagnosis of Epilepsy” ,wseas transactions on biology and biomedicine.
  8. Nandish.M, Stafford Michahial, Hemanth Kumar P, Faizan Ahmed “ Feature Extraction and Classification of EEG Signal Using Neural Network Based Techniques”, International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 4, October 2012
  9. P Bhuvaneswari, J Satheesh Kumar “Support Vector Machine Technique for EEG Signals” International Journal of Computer Applications (0975 – 8887) Volume 63– No.13, February 2013
  10. Vikramaditya Jakkula “Tutorial on Support Vector Machine (SVM)” School of EECS,Washington State University, Pullman 99164.
Index Terms

Computer Science
Information Sciences

Keywords

EEG (Electroencephalography) segmentation PCM DWT SVM.