EMD Strategy for Activation Detection in Functional MRI of the Human Brain
This article investigates 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.
functional magnetic resonance imaging empirical mode decomposition blood oxygen level dependent region growing general linear model
Tianxiang Zheng Lihua Yangt Tianzi Jiang
Department of Tourism Management Shenzhen Tourism College, Jinan University, Shenzhen, China Department of Scientific Computing and Computer Application School of Mathematics and Computational National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences, Bei
国际会议
太原
英文
559-563
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）