Last week at ICON I attended an interesting talk by Stanislas Dehaene, in which he described some work in recent papers by Jean-Rémi King; I'll highlight a few aspects (and muse a bit) here.
First, MVPA of MEG data. I've never worked with MEG data, but this paper (citation below) describes classifying (linear SVM, c=1!) trial type within-subjects, using amplitude in each of the 306 MEG sensors instead of BOLD in voxels (using all the MEG sensors is a whole-brain analysis: features span the entire brain). I should go back to (as the authors do here) referring to "MVPA" as an acronym for "multivariate pattern analysis" instead of "multi-voxel pattern analysis" to not exclude MEG analyses!
Bode & Haynes 2009.
"Temporal generalization matrices" take timepoint-by-timepoint analyses a step further: instead of training and testing on images from the same timepoint, they train on images from one timepoint, then test on images from every other timepoint, systematically. The axes are thus (within-trial) timepoint, training-set timepoint on the y-axis and testing-set timepoint on the x-axis. The diagonal is from training and testing on the same timepoint, same as the Bode & Haynes 2009-style line graphs.
Finally, note the dark blue blotch in the left-side matrix: they found clear below-chance accuracy for certain combinations of timepoints, which they discuss quite a bit in the manuscript. I'm pointing this out since below-chance accuracies are a persistent issue, and they (properly) didn't over-interpret (or hide) it in this case.
King JR, Gramfort A, Schurger A, Naccache L, & Dehaene S (2014). Two distinct dynamic modes subtend the detection of unexpected sounds. PLoS One, 9 (1) PMID: 24475052