Random segmentation based principal component analysis to remove residual MR gradient artifact in the simultaneous EEG/fMRI: A preliminary study

Hyun Chul Kim, Jong Hwan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In the electroencephalography (EEG) data simultaneously acquired with the functional magnetic resonance imaging (fMRI) data, the removal of the residual magnetic resonance (MR) gradient artifacts has been a challenging issue. To remove gradient artifacts generated from switching MR gradient field, average artifact subtraction (AAS) has been widely used. After applying the AAS method, however, residual MR gradient artifacts still remained in corrected EEG data. In this study, we proposed a novel method to remove the residual MR gradient artifacts (GAs) using random segmentation based principal component analysis (rsPCA). The performance of rsPCA was compared to that of the independent component analysis (ICA) method using data acquired from a motor imagery task. The results indicated that rsPCA could suppress further the residual MR gradient artifacts remained from the AAS step compared to the ICA method.

Original languageEnglish
Title of host publicationNeural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
Pages101-107
Number of pages7
EditionPART 3
DOIs
Publication statusPublished - 2013
Event20th International Conference on Neural Information Processing, ICONIP 2013 - Daegu, Korea, Republic of
Duration: 2013 Nov 32013 Nov 7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8228 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other20th International Conference on Neural Information Processing, ICONIP 2013
CountryKorea, Republic of
CityDaegu
Period13/11/313/11/7

Keywords

  • Electroencephalography
  • Functional magnetic resonance imaging
  • MR gradient artifact
  • Principal component analysis
  • Random segmentation
  • Simultaneous EEG/fMRI

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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