http://www.nitrc.org/forum/forum.php?forum_id=5640 <table border="0" width="100%"><tr><td align="left"/></tr></table> <p><b>A hybrid method for classifying cognitive states from fMRI data.</b></p> <p>J Integr Neurosci. 2015 Oct 12;:1550022</p> <p>Authors: Parida S, Dehuri S, Cho SB, Cacha LA, Poznanski RR</p> <p>Abstract<br/> 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.<br/> </p><p>PMID: 26455882 [PubMed - as supplied by publisher]</p> en-us Copyright 2000-2026 NITRC OSI Sun, 03 May 2026 22:31:33 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator