Most of this week's work was related to continue the development of scripts and example code files that look at possible ways of computing interesting parameters and visualizing the results of linear regression algorithms for neural times series data.
1. What did you do this week?
Thus, this week's progress is easily summarized with the following bullets and links:
- Write an example script that shows how to compute and visualize the coefficient of determination, i.e., the proportion of explained variance by a set of predictors in a linear model. The nice thing about this is that we can visualize this effects in an "EEG" fashion, i.e., in a way that MNE-users will probably will find appealing (see here).
- Other work was related on computing inferential measures for the same effects, such as p- and t-values, that might help interpret the significance of effects on a more straight forward manner.
2. What is coming up next?
I believe that we have established a good basis of analyses during the last few weeks and hope to be able to tackle second level analysis next week (i.e., the estimation of linear regression effects on set of data from different individuals).
3. Did you get stuck anywhere?
I don't feel like I got stuck with anything in particular. Although, the more I get into this GSoC-project, the more I find my self needing recap some basic linear and matrix algebra lessons. Although I remember some of these things from my schoolwork, it is from time to time challenging. Nevertheless, I feel like I'm "relearning" a lot and deepening my understanding of the mathematical basis of the tools we're trying to implement during this GSoC.