Title

A Gaussian-mixed Fuzzy Clustering Model on Valence-Arousal-related fMRI Data-Set

Authors

Authors

F. Q. Shi;P. M. Bush

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

Acta Polytech. Hung.

Keywords

fMRI; Gaussian bilateral filter; Valence-Arousal; power spectrum; density; GMM fuzzy c-mean; INDEPENDENT COMPONENT ANALYSIS; NEURAL-NETWORK; FUNCTIONAL MRI; TIME-SERIES; BRAIN; PICTURES; MEMORY; WORDS; Engineering, Multidisciplinary

Abstract

Previous medical experiments illustrated that Valence and Arousal were high corresponded to brain response by amygdala and orbital frontal cortex through observation by functional magnetic resonance imaging (fMRI). In this paper, Valence-Arousal related fMRI data-set were acquired from the picture stimuli experiments, and finally the relative Valence -Arousal feature values for a given word that corresponding to a given picture stimuli were calculated. Gaussian bilateral filter and independent components analysis (ICA) based Gaussian component method were applied for image denosing and segmenting; to construct the timing signals of Valence and Arousal from fMRI data-set separately, expectation maximal of Gaussian mixed model was addressed to calculate the histogram, and furthermore, Otsu curve fitting algorithm was introduced to scale the computational complexity; time series based Valence -Arousal related curve were finally generated. In Valence-Arousal space, a fuzzy c-mean method was applied to get typical point that represented the word relative to the picture. Analyzed results showed the effectiveness of the proposed methods by comparing with other algorithms for feature extracting operations on fMRI data-set including power spectrum density (PSD), spline, shape-preserving and cubic fitting methods.

Journal Title

Acta Polytechnica Hungarica

Volume

10

Issue/Number

8

Publication Date

1-1-2013

Document Type

Article

Language

English

First Page

85

Last Page

104

WOS Identifier

WOS:000331663700005

ISSN

1785-8860

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