Application of an improved independent component analysis to artifacts removal from EEG

Zhihong Peng*, Junping Luo

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

EEG data can be easily influenced by other components in the process of recording, which would thus interfere the analysis. Independent Component Analysis (ICA) is a valid method for blind source separation. It can estimate original signals' independent components from observed signals even the original signals and mixing model are unknown. Considering the shortcomings of the application of two ICA algorithms, FastICA and extended Infomax, to EEG artifacts removal, we propose a novel InfastICA algorithm by combing FastICA and extended Infomax. By appling to removal of the EOG artifacts from EEG, test results show that this new algorithm has no special requests to the matrix W's default values and study steps, and has a fast convergence speed, with simple operation and practical application.

Original languageEnglish
Title of host publicationProceedings of the 29th Chinese Control Conference, CCC'10
Pages2784-2787
Number of pages4
Publication statusPublished - 2010
Event29th Chinese Control Conference, CCC'10 - Beijing, China
Duration: 29 Jul 201031 Jul 2010

Publication series

NameProceedings of the 29th Chinese Control Conference, CCC'10

Conference

Conference29th Chinese Control Conference, CCC'10
Country/TerritoryChina
CityBeijing
Period29/07/1031/07/10

Keywords

  • Artifacts removal
  • Electro-oculogram (EOG)
  • Electroencephalo-graph (EEG)
  • Independent component analysis (ICA)

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