Adaptive fixed-point IVA algorithm applicable to analysis on multi-subject complex fMRI data

Abstract

The invention discloses an adaptive fixed-point IVA algorithm applicable to analysis on multi-subject complex fMRI data, and belongs to the field of biomedical signal processing. The algorithm comprises the following steps: estimating an SCV distribution of complex fMRI data by adopting an MGGD-based nonlinear function; adaptively estimating a shape parameter of an MGGD by adopting a maximum likelihood estimation method, and automatically matching the shape parameter and the variable SCV distribution; updating the MGGD-based nonlinear function in an SCV-dominated subspace to implement noise elimination of the complex fMRI data; adding a pseudo-covariance matrix of input data in an algorithm updating process, and further improving the pertinence of IVA on the complex fMRI data by directly utilizing a non-circular characteristic of the complex fMRI data. According to the algorithm, multi-subject complex fMRI data of which the noise level is high but the brain function information is most comprehensive can be effectively analyzed, and under the unfavorable conditions of great differences among subjects and low signal to noise ratio, better bases can be provided for brain function researches and brain disease diagnosis.

Description

technical field [0001] The invention relates to the field of biomedical signal processing, in particular to an analysis method for multi-subject complex functional magnetic resonance imaging (functionalmagneticresonanceimaging, fMRI) data. Background technique [0002] fMRI is an indispensable and powerful tool for current brain science research. Through the analysis of fMRI data, people can more comprehensively and deeply reveal brain function and brain mechanism. Raw acquired fMRI data is complex, including magnitude data and phase data. Among them, the noise of the phase data is relatively large, causing the noise of the complex fMRI data to be larger than that of the amplitude fMRI data. For this reason, phase fMRI data are discarded directly in most fMRI studies, and only magnitude fMRI data analysis is performed. However, more and more studies have shown that phase data contains a lot of unique and physiologically meaningful information, such as blood oxygenation le...

AI Technical Summary

Problems solved by technology

As a result, the multidimensional probability density function or nonlinear function in the existing complex IVA algorithm is only suitable for speech signals, not suitable for multi-subject complex fMRI data

[0007] Third, the SCV distribution of multi-subject complex fMRI data varies greatly

In this case, the distribution of each SCV component cannot be accurately estimated by using a fixed multidimensional probability density function or a nonlinear function

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