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Abstract

Pattern recognition methods have shown that fMRI data can reveal significant information about brain activity. For example, in the debate of how object-categories are represented in
the brain, multivariate analysis has been used to provide evidence of distributed encoding
schemes. Many follow-up studies have employed different methods to analyze human fMRI
data with varying degrees of success. In this study we compare four popular pattern recognition
methods: correlation analysis, support-vector machines (SVM), linear discriminant analysis
and Gaussian naïve Bayes (GNB), using data collected at high field (7T) with higher resolution
than usual fMRI studies. We investigate prediction performance on single trials and for averages
across varying numbers of stimulus presentations. The performance of the various algorithms
depends on the nature of the brain activity being categorized: for several tasks,
many of the methods work well, whereas for others, no methods perform above chance level.
An important factor in overall classification performance is careful preprocessing of the data,
including dimensionality reduction, voxel selection, and outlier elimination.