{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T15:17:13Z","timestamp":1781104633734,"version":"3.54.1"},"reference-count":79,"publisher":"IGI Global Scientific Publishing","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,4,1]]},"abstract":"<p>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.<\/p>","DOI":"10.4018\/ijehmc.2014040101","type":"journal-article","created":{"date-parts":[[2014,9,4]],"date-time":"2014-09-04T15:28:29Z","timestamp":1409844509000},"page":"1-26","source":"Crossref","is-referenced-by-count":0,"title":["Review of fMRI Data Analysis"],"prefix":"10.4018","volume":"5","author":[{"given":"Shantipriya","family":"Parida","sequence":"first","affiliation":[{"name":"Huawei Technologies India Private Limited, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satchidananda","family":"Dehuri","sequence":"additional","affiliation":[{"name":"Ajou University, Suwon, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"ijehmc.2014040101-0","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1109\/ISI.2008.4565056","article-title":"Swarm intelligence based rough set reduction scheme for support vector machines.","author":"A.Abraham","year":"2008","journal-title":"Proceedings of the IEEE International Conference on Intelligence and Security Informatics (ISI 2008)"},{"key":"ijehmc.2014040101-1","author":"E.Alpaydin","year":"2004","journal-title":"Introduction to machine learning"},{"issue":"3","key":"ijehmc.2014040101-2","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.bandc.2005.11.009","article-title":"Study design in fMRI: Basic principles.","volume":"60","author":"E.Amaro","year":"2006","journal-title":"Brain and Cognition"},{"key":"ijehmc.2014040101-3","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1007\/978-3-642-34713-9_31","article-title":"Real-time functional MRI classification of brain states using markov-svm hybrid models: peering inside the rt-fMRI black box","author":"A.Anderson","year":"2012","journal-title":"Machine learning and interpretation in neuroimaging"},{"key":"ijehmc.2014040101-4","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/PRNI.2012.31","article-title":"Structured sparsity models for brain decoding from fMRI data.","author":"L.Baldassarre","year":"2012","journal-title":"Proceedings of the 2012 International Workshop on Pattern Recognition in NeuroImaging (PRNI)"},{"issue":"5","key":"ijehmc.2014040101-5","first-page":"856","article-title":"Classification of MRI brain images using neuro fuzzy model.","volume":"3","author":"M. 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