A novel approach to activation detection in fMRI based on empirical mode decomposition

This article presents a novel method for activation detection in task-related functional magnetic resonance imaging (fMRI) based on the Empirical Mode Decomposition (EMD) algorithm. The basic concept stems mainly from the idea that the EMD performs well in isolating the imbedded stimulus from the activated Blood Oxygen Level Dependent (BOLD) signal. The power of the proposed method was compared with the General Linear Model (GLM), spatial Independent Component Analysis (ICA) and Region Growing (RG) methods on simulated and real datasets. Experimental results suggest an almost identical performance for the proposed method compared with the standard approach of fMRI signal detection (the GLM), which indicates that it is to become a viable alternative to fMRI analysis.