Name
Abstract | ||
|---|---|---|
We introduce a novel framework for estimating vector fields using sparse basis field expansions (S-FLEX). The notion of basis fields, which are an extension of scalar basis functions, arises naturally in our framework from a rotational in- variance requirement. We consider a regression setting as well as inverse prob- lems. All variants discussed lead to second-order cone programming formula- tions. While our framework is generally applicable to any type of vector field, we focus in this paper on applying it to solving the EEG/MEG inverse problem. It is shown that significantly more precise and neurophysiologically more plausible location and shape estimates of cerebral current sources from EEG/MEG measure- ments become possible with our method when comparing to the state-of-the-art. |
Year | Venue | Keywords |
|---|---|---|
2008 | NIPS | inverse problem,vector field |
DocType | Citations | PageRank |
Conference | 12 | 1.06 |
References | Authors | |
5 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefan Haufe | 1 | 645 | 36.63 |
| Vadim V Nikulin | 2 | 325 | 27.80 |
| Ziehe, Andreas | 3 | 617 | 72.50 |
| Klaus-Robert Müller | 4 | 12756 | 1615.17 |
| G Nolte | 5 | 535 | 50.42 |