An ica algorithm for analyzing multiple data sets

Ana S. Lukic; Miles N. Wernick; Lars Kai Hansen; Stephen C. Strother

An ica algorithm for analyzing multiple data sets

Ana S. Lukic, Miles N. Wernick, Lars Kai Hansen, Stephen C. Strother

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

121 Downloads (Orbit)

Abstract

In this paper we derive an independent-component analysis (ICA) method for analyzing two or more data sets simultaneously. Our model permits there to be components individual to the various data sets, and others that are common to all the sets. We explore the assumed time autocorrelation of independent signal components and base our algorithm on prediction analysis. We illustrate the algorithm using a simple image separation example. Our aim is to apply this method to functional brain mapping using functional magnetic resonance imaging (fMRI).

Original language	English
Title of host publication	IEEE International Conference on Image Processing.
Publisher	IEEE
Publication date	2002
Pages	821-824
Publication status	Published - 2002

Access to Document

FulltextSubmitted manuscript, 166 KB

OpenUrl availability

Full text

Cite this

@inproceedings{e859761bb9144aad8abe581aa632bc3f,

title = "An ica algorithm for analyzing multiple data sets",

abstract = "In this paper we derive an independent-component analysis (ICA) method for analyzing two or more data sets simultaneously. Our model permits there to be components individual to the various data sets, and others that are common to all the sets. We explore the assumed time autocorrelation of independent signal components and base our algorithm on prediction analysis. We illustrate the algorithm using a simple image separation example. Our aim is to apply this method to functional brain mapping using functional magnetic resonance imaging (fMRI).",

author = "Lukic, \{Ana S.\} and Wernick, \{Miles N.\} and Hansen, \{Lars Kai\} and Strother, \{Stephen C.\}",

year = "2002",

language = "English",

pages = "821--824",

booktitle = "IEEE International Conference on Image Processing.",

publisher = "IEEE",

address = "United States",

}

TY - GEN

T1 - An ica algorithm for analyzing multiple data sets

AU - Lukic, Ana S.

AU - Wernick, Miles N.

AU - Hansen, Lars Kai

AU - Strother, Stephen C.

PY - 2002

Y1 - 2002

N2 - In this paper we derive an independent-component analysis (ICA) method for analyzing two or more data sets simultaneously. Our model permits there to be components individual to the various data sets, and others that are common to all the sets. We explore the assumed time autocorrelation of independent signal components and base our algorithm on prediction analysis. We illustrate the algorithm using a simple image separation example. Our aim is to apply this method to functional brain mapping using functional magnetic resonance imaging (fMRI).

AB - In this paper we derive an independent-component analysis (ICA) method for analyzing two or more data sets simultaneously. Our model permits there to be components individual to the various data sets, and others that are common to all the sets. We explore the assumed time autocorrelation of independent signal components and base our algorithm on prediction analysis. We illustrate the algorithm using a simple image separation example. Our aim is to apply this method to functional brain mapping using functional magnetic resonance imaging (fMRI).

M3 - Article in proceedings

SP - 821

EP - 824

BT - IEEE International Conference on Image Processing.

PB - IEEE

ER -

An ica algorithm for analyzing multiple data sets

Abstract

Access to Document

OpenUrl availability

Fingerprint

Cite this