**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.
- Add
`plot_design_matrix`

function 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_regression`

function (e.g., fitting of the linear model). - Extend
`mne.datsets.limo`

module 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.