Name
Abstract | ||
|---|---|---|
•A multi-variate decomposition approach is presented, based on an approximate diagonalization of a set of matrices computed using a latent space representation.•The proposed methodology follows an algebraic approach, which is common to space, temporal or spatio-temporal blind source separation algorithms.•The resulting algorithms are applied to fMRI data sets, either to extract the underlying fMRI components or to resting state fMRI data collected for a dynamic functional connectivity analysis. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.cmpb.2017.08.019 | Computer Methods and Programs in Biomedicine |
Keywords | Field | DocType |
Blind source separation,Independent component analysis,fMRI,Resting state,Retinotopy,Spatio temporal | Singular value decomposition,Random variate,Data set,Algebraic number,Pattern recognition,Matrix (mathematics),Computer science,Resting state fMRI,Algorithm,Artificial intelligence,Dynamic functional connectivity,Blind signal separation | Journal |
Volume | Issue | ISSN |
151 | C | 0169-2607 |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Goldhacker | 1 | 4 | 1.11 |
| P Keck | 2 | 0 | 0.34 |
| A Igel | 3 | 0 | 0.34 |
| Elmar Wolfgang Lang | 4 | 260 | 36.10 |
| Ana Maria Tomé | 5 | 163 | 30.42 |