  Magnetoencephalography (MEG) has a high temporal resolution well-suited for
studying perceptual learning. However, to identify where learning happens in
the brain, one needs to ap- ply source localization techniques to project MEG
sensor data into brain space. Previous source localization methods, such as the
short-time Fourier transform (STFT) method by Gramfort et al.([Gramfort et al.,
2013]) produced intriguing results, but they were not designed to incor- porate
trial-by-trial learning effects. Here we modify the approach in [Gramfort et
al., 2013] to produce an STFT-based source localization method (STFT-R) that
includes an additional regression of the STFT components on covariates such as
the behavioral learning curve. We also exploit a hierarchical L 21 penalty to
induce structured sparsity of STFT components and to emphasize signals from
regions of interest (ROIs) that are selected according to prior knowl- edge. In
reconstructing the ROI source signals from simulated data, STFT-R achieved
smaller errors than a two-step method using the popular minimum-norm estimate
(MNE), and in a real-world human learning experiment, STFT-R yielded more
interpretable results about what time-frequency components of the ROI signals
were correlated with learning.
