Estimating Learning Effects: A Short-Time Fourier Transform Regression Model for MEG Source Localization

  • Ying YangEmail author
  • Michael J. Tarr
  • Robert E. Kass
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9444)


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 apply 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. [6] produced intriguing results, but they were not designed to incorporate trial-by-trial learning effects. Here we modify the approach in [6] 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 knowledge. 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.


Source Point Mean Square Error Sensor Noise Occipital Face Area Source Localization Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was funded by the Multi-Modal Neuroimaging Training Program (MNTP) fellowship from the NIH (5R90DA023420-08,5R90DA023420-09) and Richard King Mellon Foundation. We also thank Yang Xu and the MNE-python user group for their help.


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© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Carnegie Mellon UniversityPittsburghUSA

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