A hybrid method for classifying cognitive states from fMRI data

S. Parida, S. Dehuri, S. B. Cho, L. A. Cacha, R. R. Poznanski

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Functional magnetic resonance imaging (fMRI) makes it possible to detect brain activities in order to elucidate cognitive-states. The complex nature of fMRI data requires under-standing of the analyses applied to produce possible avenues for developing models of cognitive state classification and improving brain activity prediction. While many models of classification task of fMRI data analysis have been developed, in this paper, we present a novel hybrid technique through combining the best attributes of genetic algorithms (GAs) and ensemble decision tree technique that consistently outperforms all other methods which are being used for cognitive-state classification. Specifically, this paper illustrates the combined effort of decision-trees ensemble and GAs for feature selection through an extensive simulation study and discusses the classification performance with respect to fMRI data. We have shown that our proposed method exhibits significant reduction of the number of features with clear edge classification accuracy over ensemble of decision-trees.

Original languageEnglish
Pages (from-to)355-368
Number of pages14
JournalJournal of Integrative Neuroscience
Issue number3
Publication statusPublished - 2015 Sept 1

Bibliographical note

Publisher Copyright:
© 2015 Imperial College Press.

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)


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