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Review of fMRI Data Analysis: A Special Focus on Classification

Review of fMRI Data Analysis: A Special Focus on Classification

Shantipriya Parida, Satchidananda Dehuri
Copyright: © 2014 |Volume: 5 |Issue: 2 |Pages: 26
ISSN: 1947-315X|EISSN: 1947-3168|EISBN13: 9781466654006|DOI: 10.4018/ijehmc.2014040101
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MLA

Parida, Shantipriya, and Satchidananda Dehuri. "Review of fMRI Data Analysis: A Special Focus on Classification." IJEHMC vol.5, no.2 2014: pp.1-26. http://doi.org/10.4018/ijehmc.2014040101

APA

Parida, S. & Dehuri, S. (2014). Review of fMRI Data Analysis: A Special Focus on Classification. International Journal of E-Health and Medical Communications (IJEHMC), 5(2), 1-26. http://doi.org/10.4018/ijehmc.2014040101

Chicago

Parida, Shantipriya, and Satchidananda Dehuri. "Review of fMRI Data Analysis: A Special Focus on Classification," International Journal of E-Health and Medical Communications (IJEHMC) 5, no.2: 1-26. http://doi.org/10.4018/ijehmc.2014040101

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Abstract

Classification of brain states obtained through functional magnetic resonance imaging (fMRI) poses a serious challenges for neuroimaging community to uncover discriminating patterns of brain state activity that define independent thought processes. This challenge came into existence because of the large number of voxels in a typical fMRI scan, the classifier is presented with a massive feature set coupled with a relatively small training samples. One of the most popular research topics in last few years is the application of machine learning algorithms for mental states classification, decoding brain activation, and finding the variable of interest from fMRI data. In classification scenario, different algorithms have different biases, in the sequel performances differs across datasets, and for a particular dataset the accuracy varies from classifier to classifier. To overcome the limitations of individual techniques, hybridization or fusion of these machine learning techniques emerged in recent years which have shown promising result and open up new direction of research. This paper reviews the machine learning techniques ranging from individual classifiers, ensemble, and hybrid techniques used in cognitive classification with a well balance treatment of their applications, performance, and limitations. It also discusses many open research challenges for further research.

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