Name
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). |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/ICIP.2002.1040077 | ICIP (2) |
Keywords | Field | DocType |
biomedical imaging,autocorrelation,signal analysis,algorithm design and analysis,pca,data analysis,independent component analysis,prediction algorithms,white noise,blind source separation,brain mapping | Brain mapping,Data set,Multiple data,Pattern recognition,Functional magnetic resonance imaging,Computer science,Algorithm,Artificial intelligence,Independent component analysis,Image separation,Blind signal separation,Autocorrelation | Conference |
Citations | PageRank | References |
2 | 0.71 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ana S. Lukic | 1 | 51 | 6.32 |
| Lars Kai Hansen | 2 | 2776 | 341.03 |
| Miles N. Wernick | 3 | 595 | 61.13 |
| Stephen C. Strother | 4 | 399 | 56.31 |