Analysis of fMRI images with bi-dimensional empirical mode decomposition based-on Green's functions

作者：Al Baddai Saad^*; Al Subari Karema; Tome Ana Maria; Ludwig Bernd; Salas Gonzales Diego; Lang Elmar Wolfgang

来源：Biomedical Signal Processing and Control, 2016, 30: 53-63.

DOI：10.1016/j.bspc.2016.06.019

• Empirical mode decomposition
• Green's function
• FMRI
• SVM
• VIMFs

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.

• 出版日期2016-9