\cite{Buse_2010}uses annotators  to acquire a human-aspect readability measurement. However, the model operates as a binary classifier, though it succeeded in classifying snippets as "readable" or "not-readable" in more than 80% of the cases, it would be better if the model could point out what problems exist in the snippets and help developers to improve their code readability.
fMRI is a pretty new technique for the software engineering research, and it has a huge potential in the evaluation of new tools, software, etc. fMRI has a tight connection with EEG(Electroencephalography) since EEG has a higher temporal resolution while fMRI has a higher spatial resolution. Therefore, combining EEG and fMRI could provide complementary Spatio-temporal information for brain activity study. With the combination, we evaluate something hard to measure with only surveys, such as the task difficulties \cite{Fritz_2014}.
To help developers better understand and review code, \cite{Barnett_2015} introduced ClusterChange an automatic decomposition of changesets. Inspired by \cite{Siegmund_2017}, we could use fMRI to evaluate if this type of toolkit (like CodeBubbles, ClusterChange, etc.) could reduce the activation intensity.