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EEG Signal with Feature Extraction using SVM and ICA Classifiers

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 85 - Number 3
Year of Publication: 2014
Authors:
Chunchu Rambabu
B. Rama Murthy
10.5120/14818-3046

Chunchu Rambabu and Rama B Murthy. Article: EEG Signal with Feature Extraction using SVM and ICA Classifiers. International Journal of Computer Applications 85(3):1-7, January 2014. Full text available. BibTeX

@article{key:article,
	author = {Chunchu Rambabu and B. Rama Murthy},
	title = {Article: EEG Signal with Feature Extraction using SVM and ICA Classifiers},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {85},
	number = {3},
	pages = {1-7},
	month = {January},
	note = {Full text available}
}

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.

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