@inproceedings{8bfdbd2646d842b3a47b4b163d79ef12,
title = "Independent component analysis with feature selective filtering",
abstract = "In this contribution, we propose a feature selective filtering scheme for independent component analysis (ICA) to improve the estimation of the sources of interest (SOI), i.e., sources that have certain desired features in their sample space. As an example, we show that ICA with a smooth filtering scheme can improve the estimation of the smooth image sources from a mixture of images, as well as the estimation of a smooth visual activation map in a hybrid functional magnetic resonance imaging (fMRI) data set. Hence, the technique can potentially be used in the analysis of fMRI data to improve the ICA estimation of functional activation regions that are expected to be smooth.",
author = "Li, {Yi Ou} and T{\"u}lay Adali and Calhoun, {Vince D.}",
year = "2004",
month = dec,
day = "1",
language = "English (US)",
isbn = "0780386086",
series = "Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop",
pages = "193--202",
editor = "A. Barros and J. Principe and J. Larsen and T. Adali and S. Douglas",
booktitle = "Machine Learning for Signal Processing XIV - Proceedings of 2004 IEEE Signal Processing Society Workshop",
note = "Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop ; Conference date: 29-09-2004 Through 01-10-2004",
}