Abstract
The functional magnetic resonance imaging (fMRI) provides very useful information about the activities from different brain areas during a task. This information can be used to train a classifier and predict the sensory and motor functions and also different mental states of the subject’s brain in a particular task. Using a high resolution fMRI, normally the activities from many voxels are obtained with respect to time and not all of these voxels involve actively in a particular task. Here we propose a combination of feature selection strategies using an evolutionary computation algorithm and the support vector machines to find out those feature dimensions that are actively involved in representing the brain activities in a particular task. We show that using this lower dimensional space we can predict the cognitive state of the subjects in a particular task more accurately.
Similar content being viewed by others
References
Balconi M (2010) Neuropsychology of communication. Springer, Milan
Chang C–C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:27:1–27:27
Cortes C, Vapnik V (1995) Support-vector netwroks. Mach Learn 20:273–297
Devijver P, Kittler J (1982) Pattern recognition: a statistical approach. Prentice-Hall International, Englewood Cliffs
Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edition. Wiley-Interscience, New York
Eiben AE, Smith JE (2007) Introduction to evolutionary computing. Springer, Berlin
Fukunaga K (1990) Introduction to statistical pattern recognition, 2nd edition. Academic Press, New York
Harrison SA, Tong F (2009) Decoding reveals the contents of visual working memory in early visual areas. Nature 458:632–635
Haxby JV, Gobbini MI, Furey ML, Ishai A, Schouten JL, Pietrini P (2001) Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293(5539):2425–2430
Haynes JD, Rees G (2005) Predicting the orientation of invisible stimuli from activity in human primary visual cortex. Nat Neurosci 8:686–691
Kamitani Y, Tong F (2006) Decoding seen and attended motion directions from activity in the human visual cortex. Curr Biol 16:1096–1102
Keller TA, Just MA, Stenger VA (2001) Reading span and the time-course of cortical activation in sentence-picture verification. Annual Convention of the Psychonomic Society, Orlando
Kullback S (1959) Information theory and statistics. Wiley, New York
Mitchell T, Hutchinson R, Just M, Niculescu R.S, Pereira F, Wang X (2003) Classifying instantaneous cognitive states from fMRI data, American Medical Informatics Association Symposium
Mitchell TM, Hutchinson R, Niculescu RS, Pereira F, Wang X (2004) Learning to decode cognitive states from brain images. Mach Learn 57:145–175
Norman KA, Polyn SM, Detre GJ, Haxby JV (2006) Beyond mind-reading: multi-voxel pattern analysis of fMRI data. TRENDS Cogn Sci 10(9):424–430
Roy CS, Sherrington CS (1890) On the Regulation of the blood-supply of the brain. J Physiol 11(1–2):85–158
Theodoridis S, Koutroumbas K (2009) Pattern recognition, 4th edition. Academic Press, Amsterdam
Zhang J, Mueller ST (2005) A note on ROC analysis and non-parametric estimate of sensitivity. Psychometrika 70:203–212
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Daliri, M.R. Predicting the Cognitive States of the Subjects in Functional Magnetic Resonance Imaging Signals Using the Combination of Feature Selection Strategies. Brain Topogr 25, 129–135 (2012). https://doi.org/10.1007/s10548-011-0213-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10548-011-0213-y