Interpretability in Linear Brain Decoding
This work addresses the problem of improving interpretability in neuroimaging studies, but it is incremental as it builds on existing linear methods without introducing a new paradigm.
The authors tackled the lack of a formal definition and quantitative measure for interpretability in linear brain decoding models by proposing a simple definition and a multi-objective criterion combining interpretability and performance for model selection. Preliminary results on toy data showed that optimizing hyper-parameters based on this criterion leads to more informative linear models.
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.