PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Estimating vector fields using sparse basis field expansions
Stefan Haufe, Vadim Nikulin, Klaus-Robert Müller, Andreas Ziehe and Guido Nolte
In: Advances in Neural Information Processing Sytems (2009) MIT Press , Cambridge, US , pp. 617-624.

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.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Brain Computer Interfaces
ID Code:4268
Deposited By:Stefan Haufe
Deposited On:07 February 2009