[pymvpa] Mean decoding (for searchlight analyses)

[Michael Bannert]

Dear PyMVPA experts,

I would like to compare my MVPC accuracies obtained using all features 
in my dataset with classification accuracies using only the mean of each 
vector sample.

The idea is to compare how much information about the class label is 
represented in the mean overall activation level within a brain region 
and how much (more) information is represented in the fine-grained patterns.

For ROI analyses, I could just use a new dataset containing only the 
mean values per sample and then classify the scalars.

For searchlight analyses, however, I cannot think of an easy way to 
accomplish this. Can you think of a good way to do this?

Thanks & best,
Michael