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Indexed by:会议论文

Date of Publication:2016-03-20

Included Journals:EI、CPCI-S

Volume:2016-May

Page Number:714-718

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

Abstract: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.