1. What did you do this week?
This week I focused on putting up a GitHub repository for my GSoC project and adding some example code and auxiliary functions that will help me test and validate additions to MNE-Python’s API.
Furthermore, I made improvements to the code featured in last week’s post and extended some of it’s features.
Here a quick summary of this week’s progress:
- Set up MNE-stats repository on GitHub.
plot_design_matrixfunction for visualization of design matrices.
- Add example code for the inspection of group-level effects in the LIMO dataset.
- Propose changes to
mne.stats.linear_regressionfunction (e.g., fitting of the linear model).
mne.datsets.limomodule in MNE-Python to allow download of the complete dataset.
2. What is coming up next?
Next, I'll continue extending the
mne.stats.linear_regression function, mainly focusing on group level inference.
In particular I will work on the following aspects:
- Extend mne.stats.linear_regression function to allow for example:
- The fitting of a "first-level" or subject-level linear model over a series of time samples and recording sites for each individual in a group,
- Uses this first-level information to carry out inference o a "second-level" or group-level.
- Visualize group-level effects and confidence of prediction.
- Improve the "robustness" of this method, for instance, by enabling the algorithm to account for outliers in the dataset.
2. Did you get stuck anywhere?
Not really, this week was pretty straightforward and I feel like I'm getting a little bit of "flow" and progressing well with my project.
Although, going from first to second level analyses in linear regression might represent a sticking point in the project. In particular because this has rarely been implemented (at least in Python) for the analysis of entire magneto- and electro-encephalography datasets, which often include a wide variety of samples, recording sites and subjects.