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A Hybrid Approach for Artifacts Removal from EEG Recordings

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Alaa Eldeen M. Helal, Ahmed Farag Seddik, Ayat Allah F. Hussein
10.5120/ijca2017914301

Alaa Eldeen M Helal, Ahmed Farag Seddik and Ayat Allah F Hussein. A Hybrid Approach for Artifacts Removal from EEG Recordings. International Journal of Computer Applications 168(4):10-19, June 2017. BibTeX

@article{10.5120/ijca2017914301,
	author = {Alaa Eldeen M. Helal and Ahmed Farag Seddik and Ayat Allah F. Hussein},
	title = {A Hybrid Approach for Artifacts Removal from EEG Recordings},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {168},
	number = {4},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {10-19},
	numpages = {10},
	url = {http://www.ijcaonline.org/archives/volume168/number4/27861-2017914301},
	doi = {10.5120/ijca2017914301},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

The electroencephalogram (EEG) is a widely used traditional procedure for diagnosing, monitoring and managing neurological disorders. Many artifact types that often contaminate EEG remain a key challenge for precise diagnosis of brain dysfunctions and neurological disorders. Hence, artifact removal is intuitively required for accurate EEG analysis and treatment. This paper presents a new extensive method that can remove a wide variety of EEG artifacts based mainly on Template Matching approach including multiple signal-processing tools. The method was evaluated and validated on real EEG data, giving promising results that offer better capabilities to neurophysiologists in routine EEG examinations and diagnosis.

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Keywords

Electroencephalogram (EEG), artifacts removal, independent component analysis, wavelet, cosine similarity measure