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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2013 Vol. 3(1): 132-136 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.287

Brain Decoding Based on Functional Magnetic Resonance Imaging Using Machine Learning: A Comparative Study

Jeiran Choupan, Julia Hocking, Kori Johnson, David Reutens, and Zhengyi Yang
Abstract—Brain decoding of functional Magnetic Resonance Imaging data is a pattern analysis task that links brain activity patterns to the experimental conditions. Classifiers predict the neural states from the spatial and temporal pattern of brain activity extracted from multiple voxels in the functional images in a certain period of time. The prediction results offer insight into the nature of neural representations and cognitive mechanisms and the classification accuracy determines our confidence in understanding the relationship between brain activity and stimuli. In this paper, we compared the efficacy of three machine learning algorithms: neural network, support vector machines, and conditional random field to decode the visual stimuli or neural cognitive states from functional Magnetic Resonance data. Leave-one-out cross validation was performed to quantify the generalization accuracy of each algorithm on unseen data. The results indicated support vector machine and conditional random field have comparable performance and the potential of the latter is worthy of further investigation.

Index Terms—Brain decoding, functional magnetic resonance imaging, neural network, support vector machine, conditional random field.

The authors are with the University of Queensland, Brisbane, Australia (e-mail: j.choupan@uq.edu.au; j.hocking@uq.edu.au; kori.johnson@cai.uq.edu.au; david.reutens@cai.uq.edu.au; steven.yang@itee.uq.edu.au).

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Cite:Jeiran Choupan, Julia Hocking, Kori Johnson, David Reutens, and Zhengyi Yang, "Brain Decoding Based on Functional Magnetic Resonance Imaging Using Machine Learning: A Comparative Study," International Journal of Machine Learning and Computing vol. 3, no. 1, pp. 132-136, 2013.

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