Liu, Hongbo and Abraham, Ajith and Zhang, Weishi and McLoone, Sean F. (2011) A swarm-based rough set approach for FMRI data analysis. International Journal of Innovative Computing, Information and Control, 7 (6). pp. 3121-3132. ISSN 1349-4198

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Abstract

The functional Magnetic Resonance Imaging (fMRI) is one of the most important tools for exploring the operation of the brain as it allows the spatially localized characteristics of brain activity to be observed. However, fMRI studies generate huge volumes of data and the signals of interest have low signal to noise ratio making its analysis a very challenging problem. There is a growing need for new methods that can efficiently and objectively extract the useful information from fMRI data and translate it into intelligible knowledge. In this paper, we introduce a swarm-based rough set approach to fMRI data analysis. Our approach is based on exploiting the power of particle swarm optimization to discover the feature combinations in an efficient manner by observing the change in positive region as the particles proceed through the search space. The approach supports multi-knowledge extraction. We evaluate the performance of the algorithm using benchmark and fMRI datasets. The results demonstrate its potential value for cognition research.

Item Type:	Article
Keywords:	Particle swarm; Swarm intelligence; Multi-knowledge; fMRI;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering
Item ID:	3870
Depositing User:	Sean McLoone
Date Deposited:	17 Sep 2012 13:40
Journal or Publication Title:	International Journal of Innovative Computing, Information and Control
Publisher:	ICIC International
Refereed:	Yes
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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