Earticle

초록

영어

New breakthroughs were taken from the research of affective curve extracting from sequence-concentrated functional Magnetic Resonance Imaging (fMRI) images, and the emotional responses of human brain were hidden in these fMRI dataset; the purpose of this paper is to acquire critical features from fMRI images. The fMRI experiments were given by a certain theme emotion stimuli; firstly, component operations under bilateral filtering were applied for fMRI images’ morphological segmenting which reduced the computational space, for that the calculation was not based on the whole brain space. Operated by Fast Fourier Transform (FFT), fMRI images relative to functional area of human brain were pre-processed. Finally, time series based Power Spectrum Density (PSD) was founded by using an improved shape preserving fitting algorithm, and affective curves were acquired subsequently. The results showed the effectiveness of the proposed methodologies in this paper by comparing with cubic fitting and 5-th polynomial fitting operations. Experimental results also showed that this method was effective and efficient; the shaper preserving model had the lowest residual error that reflected the brain's emotional response curve adequately. The proposed methods have potential applications in the study of human-machine emotion interactions.

목차

Abstract
 1. Introduction
 2. Preliminaries and Methods
  2.1. Preprocessing De-noising and Segmenting on fMRI Images
  2.2. Time Series Foundation of fMRI Power Spectrum Density
  2.3. Shape Preserving Fitting
 3. Results and Discussion
 4. Conclusions and Future Works
 Acknowledgements
 References

키워드

저자

• Fuqian Shi [ School of Information & Engineering, Wenzhou Medical University, Wenzhou, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605