Paper Info
Title
Analysis of fMRI images with bi-dimensional empirical mode decomposition based-on Green's functions.
Abstract
We present a new method for decomposing two-dimensional data arrays with empirical mode decomposition (EMD). It performs envelope surface interpolation based on Green's functions in tension (GiT) to extract bi-dimensional intrinsic mode functions (BIMFs). The new method is called GiT-BEMD and outperforms existing bi-dimensional ensemble EMD (BEEMD) variants in terms of computational costs and quality of extracted intrinsic modes. More specifically, it is easy to implement, much faster than BEEMD, very robust and free from processing artifacts. GiT-BEMD is applied to fMRI data recorded during a contour integration task. Features extracted from resulting volume intrinsic mode functions (VIMFs) achieve higher classification accuracy compared to the canonical BEEMD. The new method thus provides a valuable alternative to existing mode decomposition methods for analyzing images. (C) 2016 Elsevier Ltd. All rights reserved.
Year
DOI
Venue
2016
10.1016/j.bspc.2016.06.019
Biomedical Signal Processing and Control
Keywords
Field
DocType
Empirical mode decomposition,Green's function,FMRI,SVM,VIMFs
Green's function,Pattern recognition,Computer science,Support vector machine,Methods of contour integration,Interpolation,Green S,Artificial intelligence,Hilbert–Huang transform
Journal
Volume
ISSN
Citations 
30
1746-8094
0
PageRank 
References 
Authors
0.34
11
6
Name
Order
Citations
PageRank
Saad Al-Baddai172.48
Karema Al-Subari272.14
Ana Maria Tomé316330.42
bernd ludwig443642.67
Diego Salas-Gonzales500.34
Elmar Wolfgang Lang626036.10