点击次数：

论文类型：会议论文

发表时间：2016-03-20

收录刊物：EI、CPCI-S

卷号：2016-May

页面范围：714-718

关键字：Terms Independent vector analysis (IVA); complex-valued fMRI data; non-circularity; subspace; nonlinearity

摘要：Independent vector analysis (IVA) has exhibited great potential for the group analysis of magnitude-only fMRI data, but has rarely been applied to native complex-valued fMRI data. We propose an adaptive fixed-point IVA algorithm by taking into account the extremely noisy nature, large variability of the source component vector (SCV) distribution, and non-circularity of the complex-valued fMRI data. The multivariate generalized algorithm over a complex-valued IVA-G algorithm and several circular and noncircular fixed-point IVA variants.