O Halchenko, Yaroslav and Pearlmutter, Barak A. and Hanson, Stephen Jose and Zaimi, Adi (2004) Fusion of functional brain imaging modalities via linear programming. Biomedical Engineering / Biomedizinische Technik, 48 (2). pp. 102-104. ISSN 0013-5585

Preview

Download (112kB) | Preview

Abstract

Proposed method makes a number of simplifying assumptions which convert the EEG/FMRI integration problem into optimization of a convex function, of a form amenable to efficient solution as a very sparse linear programming (LP) problem. The assumptions made in doing this are, surprisingly, in general somewhat more robust than those generally used to cast EEG/FMRI integration as optimization of a non-convex function not amenable to efficient global optimization. This is because the L1 norm used here corresponds to a more robust statistical estimator than the L2 normal generally used For this reason, even though this technique results in a tractable global optimization, it is more robust to non-Gaussian noise and outliers than approaches that make the Gaussian noise assumption [1]. Current poster presents formulation of the problem together with results obtained on artificial data.

Item Type:	Article
Keywords:	Fusion; functional brain imaging; modalities; linear programming;
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	12678
Depositing User:	Barak Pearlmutter
Date Deposited:	01 Apr 2020 10:35
Journal or Publication Title:	Biomedical Engineering / Biomedizinische Technik
Publisher:	De Gruyter
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

Repository Staff Only(login required)

Item control page

Downloads

Origin of downloads