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

This week I was able to make some good progress on the group-level inference part for my GSoC project.

This included:

- Estimate group-levels effects of a continuous variable for the full data space using linear-regression.
- First, this approach required the fitting of a linear regression model for each subject in the data set (i.e., first level analysis) and extracting the estimated beta-coefficients for the variabale in question. This part of the analysis picked up on the tools we've been working during the last few weeks.
- Second, with the beta-coefficents form each subject (i.e., the original betas), a bootstrap-t (or studentized bootstrap, see for instance here) was carried out. Here, t-values where calculated for each "group of betas" sampled with replacement from the original betas. These t-values where then used to estimate more robust confidence intervals and provide a measure of "significance" or consistency of the observed effects on a group level.
- For further discussion, see this PR on GitHub.

**2. What is coming up next?**

Next week, I will be working on an extension of this analysis technique for other group-level hypothesis-testing scenarios that can be derived from the linear regression framework.

In addition, one challenge for the next few weeks relies on the estimation of group-level p-values (significance testing) and correcting these for multiple testing. I particular we want to use spatiotemporal clustering techniques and bootstrap to achieve this.

**3. Did you get stuck anywhere?**

I wouldn't say I was stuck with anything in particular. Although, understanding the bootstrap-t technique and its implementation for the analysis of neural time-series data was somewhat challenging and required some more reading than the usual. However, after discussion and review with my mentors, I feel confident and believe our advancement are going in the right direction.