Independent component analysis and beyond in brain imaging: EEG, MEG, fMRI, and PET

J. C. Rajapakse, A. Cichocki, V. D. Sanchez A

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

22 Citations (Scopus)

Abstract

There is an increasing interest in analyzing brain images from various imaging modalities, that record the brain activity during functional task, for understanding how the brain functions as well as for the diagnosis and treatment of brain disease. Independent component analysis (ICA), an exploratory and unsupervised technique, separates various signal sources mixed in brain imaging signals such as brain activation and noise, assuming that the sources are mutually independent in the complete statistical sense. This paper summarizes various applications of ICA in processing brain imaging signals: EEG, MEG, fMRI or PET. We highlight the current issues and limitations of applying ICA in these applications, current, and future directions of research.

Original languageEnglish
Title of host publicationICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing
Subtitle of host publicationComputational Intelligence for the E-Age
EditorsJagath C. Rajapakse, Xin Yao, Lipo Wang, Kunihiko Fukushima, Soo-Young Lee
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages404-412
Number of pages9
ISBN (Electronic)9810475241, 9789810475246
DOIs
Publication statusPublished - 2002
Externally publishedYes
Event9th International Conference on Neural Information Processing, ICONIP 2002 - Singapore, Singapore
Duration: 18 Nov 200222 Nov 2002

Publication series

NameICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age
Volume1

Conference

Conference9th International Conference on Neural Information Processing, ICONIP 2002
Country/TerritorySingapore
CitySingapore
Period18/11/0222/11/02

Keywords

  • Blind signal and image processing
  • higher order statistics
  • independent component analysis
  • statistical independence

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