### 1. What did you do this week?

- As this was the final week of GSoC, I have written and posted a final report of the project here.
- In addition, I made a major overhaul of the project's website. Wich now contains a "gallery of examples" for some of major advancements and tools developed during the GSoC period.
- See this PR for a more detailed list of contributions made this week.

### 2. What is coming up next?

- There are a couple of open questions that concern the integration of these tools and analysis techniques to MNE's API.
- For instance, we've been using scikit-learn's linear regression module to fit the models. One of the main advantages of this approach consists in having a linear regression "object" as output, increasing the flexibility for manipulation of the linear model results, while leaving MNE's linear regression function untouched (for now). However, we believe that using a machine learning package for linear regression might lead to confusion among users on the long run.
- Thus, the next step is to discuss possible ways of integration to MNE-Python. Do we want to modify, simplify, or completely replace MNE's linear regression function to obtain similar output..

I really enjoyed working on this project during the summer and would be glad to continue working on extending the linear regression functionality of MNE-Python after GSoC.

### 3. Did you get stuck anywhere?

- Not really. Although the final week included a lot of thinking about what the most practical API might be for the tools developed during the GSoC period. We want to continue this discussion online (see here) and hopefully be able to fully integrate this advancements in the released version of MNE-Python soon.

Thanks for reading and please feel free to contribute, comment or post further ideas!