Title（问题）：Independent Component Analysis Involving Autocorrelated Sources with Application to Functional Magnetic Resonance Imaging

Speaker（报告人）：Prof.Haipeng Shen,The University of North Carolina at Chapel Hill

Time（时间）：2012年6月28日（周四）下昼03:15-04:15

Place（所在）：伟易博新楼217课堂

Abstract（摘要）：Independent component analysis (ICA) is an effective data-driven method for blind source separation. It has been successfully applied to separate source signals of interest from their mixtures. Most existing ICA procedures are carried out by relying solely on the estimation of the marginal density functions. In many applications, correlation structures within each source also play an important role besides the marginal distributions. One important example is functional magnetic resonance imaging (fMRI) analysis where the brain-function-related signals are temporally correlated. I shall talk about a novel ICA approach that fully exploits the correlation structures within the source signals. Our methodology is described and implemented using spectral density functions under the Whittle likelihood framework. The performance of the proposed method will be illustrated through extensive simulation studies and real fMRI application. The numerical results indicate that our approach outperforms several popular methods including the most widely used fastICA algorithm.

http://www.unc.edu/~haipeng/