Release Time:2019-03-10  Hits:

Indexed by: Conference Paper

Date of Publication: 2016-03-20

Included Journals: CPCI-S、EI

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.