@inproceedings{9b099773f2e846c7aff9851a97dce1e3,
title = "Semi-blind kurtosis maximization algorithm applied to complex-valued fMRI data",
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.",
keywords = "ICA, complex-valued ICA, fMRI, kurtosis maximization, semi-blind ICA",
author = "Lin, {Qiu Hua} and Wang, {Jia Cheng} and Gong, {Xiao Feng} and Wu, {Jian Lin} and Chen, {Jun Yu} and Calhoun, {Vince D.}",
year = "2011",
month = dec,
day = "5",
doi = "10.1109/MLSP.2011.6064555",
language = "English (US)",
isbn = "9781457716232",
series = "IEEE International Workshop on Machine Learning for Signal Processing",
booktitle = "2011 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2011",
note = "21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011 ; Conference date: 18-09-2011 Through 21-09-2011",
}