EMD strategy for activation detection in functional MRI of the human brain

This article in vestigates how the Empirical Mode Decomposition (EMD) algorithm, a newly-developed and effective technique in the fields of signal processing and time-frequency analysis, is introduced and applied to activation detection in task-related functional Magnetic Resonance Imaging (fMRI) of the human brain. The method is illustrated using fMRI data simulated under one paradigm as well as one real dataset, and its performance is compared to those of a leading model based (General Linear Model - GLM) and a leading data-driven (Region Growing Method - RGM) approach. It is concluded that the proposed method achieves much better performance than the computationally inefficient RGM and is slightly more sensitive than GLM. Experimental results support its efficiency and sensitivity, which indicates that it is to become a viable alternative to the fMRI analysis.