Feature-selective ICA and its convergence properties

Yi Ou Li, Tülay Adali, Vince D. Calhoun

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

Abstract

We present a projection-based framework for a feature-selective independent component analysis (FS-ICA) scheme and study its convergence property for two ICA algorithms, FastICA and Infomax. As examples, we implement bandpass filter as the feature-selective filter to improve the estimation of a bandpass signal from the mixtures and a periodic task-related time course embedded in the functional Magnetic Resonance Imaging (fMRI) data. Hence, we demonstrate that the proposed method can incorporate a priori information into ICA to effectively improve estimation of the underlying components of practical interest, such as periodic time courses and smooth brain activation areas in fMRI data.

Original languageEnglish (US)
Title of host publication2005 IEEE ICASSP '05 - Proc. - Design and Implementation of Signal Proces.Syst.,Indust. Technol. Track,Machine Learning for Signal Proces. Education, Spec. Sessions
PublisherInstitute of Electrical and Electronics Engineers Inc.
PagesV265-V268
ISBN (Print)0780388747, 9780780388741
DOIs
StatePublished - Jan 1 2005
Externally publishedYes
Event2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA, United States
Duration: Mar 18 2005Mar 23 2005

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
VolumeV
ISSN (Print)1520-6149

Other

Other2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
CountryUnited States
CityPhiladelphia, PA
Period3/18/053/23/05

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Li, Y. O., Adali, T., & Calhoun, V. D. (2005). Feature-selective ICA and its convergence properties. In 2005 IEEE ICASSP '05 - Proc. - Design and Implementation of Signal Proces.Syst.,Indust. Technol. Track,Machine Learning for Signal Proces. Education, Spec. Sessions (pp. V265-V268). [1416291] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. V). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2005.1416291