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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
10.5120/14818-3046

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 = { https://ijcaonline.org/archives/volume85/number3/14818-3046/ },
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
Abstract

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.

References
  1. E. Tamil,"Electroencephalogram (EEG) Brain Wave FeatureExtraction Using Short Time Fourier Transform", Faculty of ComputerScience and Information Technology, University of Malaya,2007.
  2. J. Lee, D. Tan, "Using a Low-Cost Electroencephalograph for TaskClassification in HCI Research", UIST'06, Montreux, Switzerland, October 15–18, 2006.
  3. G. Molina, "Joint Time-Frequency-Space Classification of EEG ina Brain-Computer Interface Application", EURASIP Journal on AppliedSignal Processing, Vol. 7, pp. 713–729, 2003.
  4. A. Akrami, "EEG-Based Mental Task Classification: Linear andNonlinear classification of Movement Imagery", in proceedings of theIEEE Engineering in Medicine and Biology 27thAnnualConference Shanghai, China, September 1-4, 2005.
  5. H. BehnamA, A. SheikhaniB, M. MohammadiC, M. NoroozianD, P. Golabie, "Analyses of EEG background activity in Autism disorder withfast Fourier transform and short time Fourier transform", InternationalConference on Intelligent and Advanced Systems,2007.
  6. AbdulhamitSubasi, M. Ismail Gursoy, "EEG signal classification using PCA, ICA, LDA and support vector machines", Expert Systems with Applications, Vol. 37, pp. 8659–8666, 2010.
  7. Cao, L. J. , Chua, K. S. , Chong, W. K. , Lee, H. P. , &Gu, Q. M. , "A comparison ofPCA, KPCA and ICA for dimensionality reduction in support vector machine", Neurocomputing, 55, pp. 321–336, 2003.
  8. Subasi, A. , "EEG signal classification using wavelet feature extraction and amixture of expert model", Expert Systems with Applications, 32, pp. 1084–1093, 2007.
  9. Ubeyli, E. D. , "Analysis of EEG signals by combining eigenvector methods andmulticlass support vector machines", Computers in Biology and Medicine, 38,pp. 14–22, 2008.
  10. Wang, X. , Paliwal, K. K. , "Feature extraction and dimensionality reductionalgorithms and their applications in vowel recognition", Pattern Recognition, 36,pp. 2429–2439, 2003.
  11. Widodo. A. , Yang. B. , "Application of nonlinear feature extraction andsupport vector machines for fault diagnosis of induction motors", Expert Systemswith Applications, 33, pp. 241–250, 2007.
  12. Carlos Guerrero-Mosquera, Michel Verleysen and Angel Navia Vazquez, "EEG feature selection using mutualinformation and support vectormachine: A comparative analysis", 32ndAnnual International Conference of the IEEE EMBSBuenos Aires, Argentina, August 31st- September 4th, 2010.
  13. Gomez V. Vanessa, Verleysen Michel and Jerome Fleury, "Informationtheoric feature selection for functional data classification",Neurocomputing,Vol. 72, pp. 3580–3589, 2009.
  14. Guerrero-Mosquera C. , MalandaTrigueros A. , Iriarte Franco J. andNavia Vazquez Angel, "New feature extraction approach for epilepticEEG signal detection using time-frequency distributions",Med BiolEngComputer, Vol. 48, pp. 321–330, 2009.
  15. OcakHasan, "Optimal classification of epileptic seizures in EEG usingwavelet analysis and genetic algorithm",Signal processing, Vol. 88, pp. 1858–1867, 2008.
  16. MarcinKoOdziej, AndrzejMajkowski,Remigiusz J. Rak, "A new method of feature extraction from EEG signal for braincomputerinterface design", Przegl D Elektrotechniczny, ISSN 0033-2097, R. 86 NR 9/2010.
  17. MiHye Song, Jeon Lee, Sung Pil Cho, KyoungJoung Lee, and Sun Kook Yoo, "Support Vector Machine Based Arrhythmia ClassificationUsing Reduced Features", International Journal of Control, Automation and Systems, Vol. 3, No. 4, pp. 571-579, December 2005.
  18. V. V. Shete, SachinElgandelwar, Sapna Sonar, AshwiniCharantimath, Dr. V. D. Mytri, "Detection of K-Complex in Sleep EEG Signal using Support Vector Machine", International Journal of Scientific & Engineering Research, Vol. 3, Issue 6, No. 1, ISSN 2229-5518, June-2012.
  19. Sarah M. Hosni, Mahmoud E. Gadallah, Sayed F. Bahgat, Mohamed S. AbdelWahab, "Classification of EEG Signals Using DifferentFeature Extraction Techniques for Mental-Task BCI", IEEE Transactions, 2007.
  20. Mohammad H. Alomari, AyaSamaha, and KhaledAlKamha, "Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning", International Journal of Advanced Computer Science and Applications(IJACSA), Vol. 4, No. 6, 2013.
  21. KavitaMahajan, M. R. Vargantwar, Sangita M. Rajput, "Classification of EEG using PCA, ICA and Neural Network", International Journal of Engineering and Advanced Technology (IJEAT), Volume-1, Issue-1, October 2011.
Index Terms

Computer Science
Information Sciences

Keywords

EEG signals Fast ICA PCA SVM and Hardware Architecture