  Improving the interpretability of brain decoding approaches is of primary
interest in many neuroimaging studies. Despite extensive studies of this type,
at present, there is no formal definition for interpretability of brain
decoding models. As a consequence, there is no quantitative measure for
evaluating the interpretability of different brain decoding methods. In this
paper, we present a simple definition for interpretability of linear brain
decoding models. Then, we propose to combine the interpretability and the
performance of the brain decoding into a new multi-objective criterion for
model selection. Our preliminary results on the toy data show that optimizing
the hyper-parameters of the regularized linear classifier based on the proposed
criterion results in more informative linear models. The presented definition
provides the theoretical background for quantitative evaluation of
interpretability in linear brain decoding.
