Although many software packages are nowadays much easier to get up and running than, say, a few years ago, some of them can still cause the occasional headache. For instance, I spent the better part of a day getting the Magenta toolbox (part of the TensorFlow ecosystem) for modelling and generating music using machine learning to work on the GPUs of the high-performance compute (HPC) cluster here at the Donders Institute (Donders Centre for Cognitive Neuroimaging; DCCN). Since I don't want anyone else to have to go through the same pains as I did, I thought I'd document my experiences here. (Also, I'll have to explain this to at least one of my students soon, so why not blog about it instead?)
My paper together with Floris de Lange about how we learn from spatial context, and whether this depends on conscious awareness, was recently published in the Journal of Neuroscience. Read a quick summary here.
Floris de Lange and I recently had a paper accepted for publication in The Journal of Neuroscience. Once it's actually published, I'll probably post more about it, but for now, I just want to get the manuscript, code, and data out there, mainly because I'll be presenting this stuff at the Society for Neuroscience next week.
Update: view a more detailed blog post about the content here.
This is the second installment (out of probably three) of blog posts based on the data visualization workshop I gave for the Language in Interaction consortium. (Part 1 was on plotting ERP/Fs, or 1D data over time with uncertainty.). This time I'll go over some things you can do to improve the appearance of time-frequency representations (TFRs), often of oscillatory power. The customizations I'm describing here will be a bit more concerned with aesthetics and personal preference than with actually "showing the data" (like in part 1), but I still think/hope this will be useful to some.
I recently (well, 25 June 2018) taught a data visualization workshop as part of the Language in Interaction consortium at the Donders Institute in Nijmegen. As part of my workshop, I thought I'd go over some fairly simple ways in which people could customize (or dare I say: improve?) some commonly used plots when analyzing MEG or EEG data. Since it would be a waste to have all that preparation for the workshop go to waste on just the small audience that was there that day, I'll post my code with some explanations here on my blog as well. Today: how to better visualize plots like the event-related potential or field. Or, more generally: how to visualize 1D data over time.
Don't you just hate when you have an [cci]MxN[/cci] struct array in Matlab, where each element contains some [cci]PxK[/cci] data field, and you just really want to have all that data in a single [cci]PxKxMxN[/cci] array? OK, maybe this doesn't happen to you very frequently, but it has happened to me. In fact, it has happened so often that I decided to write a (very simple) general solution to this problem.