Over 100 years, many neuroimaging techniques have been developed to study the functional organization of the human brain. Among these techniques, functional magnetic resonance imaging (fMRI) is the most useful way because of its ability to safely and noninvasively imaging the brain activity. Moreover, its spatial and temporal resolutions are well matched to local changes in oxygen levels over time which in turn reflects the amount of local brain activity. As the analysis of fMRI data is exceedingly complex, we require the use of sophisticated techniques from image processing and statistical modeling to detect transient cognitive states. So it is possible to track hidden cognitive states. These approaches will be applicable to diagnosing medical problems such as Alzheimer's disease.

Many studies have employed varied supervised methods to compare classifiers performance on human fMRI data [1–4]. Besides merits of using supervised learning such as...

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