Empirical Mode Decomposition in Data-Driven fMRI Analysis

Empirical Mode Decomposition has emerged in recent years as a promising data analysis method to adaptively decompose non-linear and non-stationary signals. Here we introduce multi-EMD, to be used where there are many thousands of signals to analyse and compare, such as is common in the analysis of f...

Full description

Saved in:
Bibliographic Details
Main Authors: McGonigle, J, Mirmehdi, M, Malizia, A L
Format: Conference Proceeding
Language:English
Subjects:
Brain
Clustering algorithms
Clustering methods
Magnetic resonance
Magnetic resonance imaging
Phantoms
Pixel
Signal resolution
Time-frequency analysis
Wavelet analysis
Citations: Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Empirical Mode Decomposition has emerged in recent years as a promising data analysis method to adaptively decompose non-linear and non-stationary signals. Here we introduce multi-EMD, to be used where there are many thousands of signals to analyse and compare, such as is common in the analysis of functional neuroimages. The number of component signals found through Empirical Mode Decomposition varies at each location in the brain. We seek to rearrange these components so that they may be compared to others at a similar temporal scale. This is a data-driven process based on grouping those components which have similar dominant frequencies to target frequencies which have been found to be most common from the initial decomposition. This new set of rearranged components is then clustered so that regions behaving synchronously at each temporal scale may be discovered. Results are presented for both simulated and real data from a functional MRI experiment.
DOI:10.1109/WBD.2010.14