This is one of the most readable, non-mathematical introductions to SVMs I've ever run across in the literature. He uses microarray data for the example, but replace "measured mRNA expression level" with "BOLD activation" and the situation is pretty much the same as in MVPA.
He includes a description of what it means to have a soft (or hard) margin; I'll add that the conventional c=1 used with linear classifiers for MVPA corresponds to a fairly soft margin: many training points are allowed to be on the 'wrong' side of the decision boundary.
William S Noble (2006). What is a support vector machine? Nature Biotechnology, 24, 1565-1567 DOI: 10.1038/nbt1206-1565
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