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 invariance requirement. We consider a regression setting as well as inverse problems. All variants discussed lead to second-order cone programming formulations. 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 measurements become possible with our method when comparing to the state-of-the-art.
| Original language | English |
|---|---|
| Title of host publication | Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference |
| Pages | 617-624 |
| Number of pages | 8 |
| Publication status | Published - 2009 Dec 1 |
| Externally published | Yes |
| Event | 22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 - Vancouver, BC, Canada Duration: 2008 Dec 8 → 2008 Dec 11 |
Other
| Other | 22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 |
|---|---|
| Country | Canada |
| City | Vancouver, BC |
| Period | 08/12/8 → 08/12/11 |
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ASJC Scopus subject areas
- Information Systems
Cite this
Estimating vector fields using sparse basis field expansions. / Haufe, Stefan; Nikulin, Vadim V.; Ziehe, Andreas; Muller, Klaus; Nolte, Guido.
Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 2009. p. 617-624.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Estimating vector fields using sparse basis field expansions
AU - Haufe, Stefan
AU - Nikulin, Vadim V.
AU - Ziehe, Andreas
AU - Muller, Klaus
AU - Nolte, Guido
PY - 2009/12/1
Y1 - 2009/12/1
N2 - 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 invariance requirement. We consider a regression setting as well as inverse problems. All variants discussed lead to second-order cone programming formulations. 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 measurements become possible with our method when comparing to the state-of-the-art.
AB - 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 invariance requirement. We consider a regression setting as well as inverse problems. All variants discussed lead to second-order cone programming formulations. 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 measurements become possible with our method when comparing to the state-of-the-art.
UR - http://www.scopus.com/inward/record.url?scp=77954643957&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77954643957&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:77954643957
SN - 9781605609492
SP - 617
EP - 624
BT - Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
ER -