An ICA algorithm for analyzing multiple data sets

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).

• Type: Conference
• Year: 2002
• Where: ICIP
• Authors: Ana S. Lukic, Lars Kai Hansen, Miles N. Wernick, Stephen C. Strother
• Keywords: Assumed Time Autocorrelation, Functional Magnetic Resonance, ICIP 2002, Image Processing, Various Data Sets