ENH: stats for ERF decoding and time generalization

With @kingjr we're currently thinking about the right way to handle cross-trial significance testing for the analyses demonstrated in our decoding examples. This is basically overlapping with one of our GSOC topics, and the discussion in the comments over here. Now practically, permutation testing or bootstrapping seems pretty heavy, if it means that you resample every model you fit. This will render these kind of analyses intractable. On the flip side there seems to be a problem in using any test that makes assumptions about the degrees of freedom, as for single trial probability output, for example, the samples are not independend because of cross-validation. It seems, instead of bootstrapping the entire decoding or incorporating permutation testing into cross-validation, as suggested by Martin Hebart (first comment here), we could just compute a permutation test post-hoc on the predictions. This would be much more tractable.
I was wondering what you think might be the right way to go. Also for the GSOC project it would be nice to have some discussion about this.

cc @agramfort @Eric89GXL @banilo

Start with the issue discussion, the linked GSOC topic, and the referenced cross-trial significance-testing discussion. Compare post-hoc permutation testing with bootstrapping and permutation within cross-validation, then establish the statistically appropriate approach for decoding and time-generalization analyses. Done means the project has an agreed method and a defined implementation scope.