Semi-blind kurtosis maximization algorithm applied to complex-valued fMRI data

Qiu Hua Lin, Jia Cheng Wang, Xiao Feng Gong, Jian Lin Wu, Jun Yu Chen, Vince D. Calhoun

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

Abstract

The complex kurtosis maximization (KM) algorithm is an efficient algorithm for separating mixtures of circular signals and noncircular signals, which are the typical characteristic in real situations. Based on the fixed-point KM algorithm, we here propose a semi-blind complex ICA algorithm by incorporating the magnitude information about a specific signal into the cost function of KM as an inequality constraint. The proposed algorithm is tested using both synthetic signals including circular and noncircular complex-valued sources and real complex-valued functional magnetic resonance imaging (fMRI) data. Performance is compared to several standard complex ICA algorithms and an additional semi-blind complex ICA algorithm based on gradient KM algorithm. The results show that the proposed semi-blind complex ICA algorithm can largely improve the performance of separation. Significant improvement is shown for the detection of task-related components from the complex-valued fMRI data, which are complete but much noisier than the magnitude-only fMRI data.

Original languageEnglish (US)
Title of host publication2011 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2011
DOIs
StatePublished - Dec 5 2011
Externally publishedYes
Event21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011 - Beijing, China
Duration: Sep 18 2011Sep 21 2011

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing

Other

Other21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011
CountryChina
CityBeijing
Period9/18/119/21/11

Keywords

  • ICA
  • complex-valued ICA
  • fMRI
  • kurtosis maximization
  • semi-blind ICA

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

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