We recently moved to a new building and the labs moved with us. We did not only use this opportunity to get rid of old computers and monitors, but also to thoroughly check that our timing is correct. This work was mostly done by Ule Diallo, an intern from Marburg University. We were very lucky to have him! TLDR; We searched for fixed and jittered delays in our setup. We measured many monitors with two luminance sensors. We also checked our parallel-ports trigger-timing. We did not find jittered delays, the fixed delays are, as expected, only by the monitors. Why…

Update 01.08.2016 after further discussion we discovered that the correlation matrix was a bit off. This is because I plotted the correlation of the weights without the sphering (=whitening) step. Sphering removes the correlation between the components. I added a plot with the correlation after sphering and updated the topographies. Goal In EEG blind source separation using ICA, understand the difference between the mixing matrix and the unmixing matrix. brief introduction The blind source separation problem in the context of EEG can be understood as follows: $$ mixing * sourceactivity = EEGdata $$ In words: We have some sources active…

This is taken and copied from github/behinger/permtest: I couldn’t find a simple permutation test for matlab, thus I decided to implement it on my own. I got inspired by the “statmod”-R package and followed Phipson & Smyth 2010’s recommendations. How to Use: [code lang=matlab] permtest(randi([1,5],100),randi([3,8],100)) permtest(randi([1,5],100),randi([3,8],100),10000) permtest(randi([1,5],100),randi([3,8],100),[],\"conservative\") % usually the fastest implementation [/code] Permutations / Randomization I chose to do permutations with repeats. This complicates p-values, but in the end I found it to be faster (and scales better). P-Values There are three methods implemented to get the p-value: b = permutations with greater value than test-statistic m_t = number…

A small fact got me into trouble (spoiler: the intercept in effects coding represents the mean of conditions, not the data-mean). Update 2016-08-11 I found a nice paper that remedies the last point: weighted effects coding Update 2018-11-30 If you enjoyed reading this post, check out my sucessor post on Effect/Sum Coding Update 2019-07-22 I highly recommend this recent paper: How to capitalize on a priori contrasts in linear (mixed) models: A tutorial (2019) which will explain all things of this blogpost in more space, more examples, more code and with better words! The goal We try to model two…

I sometimes give improvised lectures to a small circle of students. These are some of the explanations I use. The goal Find time-points in a time series that differ between two conditions over subjects without performing single independent tests for each time-point. Step by Step Guide In this tutorial, we apply the cluster-permutation test to a single time-series. This could be a single channel, an average over channels, or one “source/component” from an ICA decomposition. We can make use of the neighbour-relation of time: $t$ has the neighbors $t-1$ and $t+1$. We don’t expect the time points to be independent,…

Here in the institute we have a Sun Grid Engine available. It is a tool to post computing-jobs on other workspaces (we have I think up to 60 available). There are certain commands and things that I do regularly which I tend to forget after half a year, or which might be useful for orthes. Show all jobs that are running on the grid qstat -u \* or alternativly for a single user qstat -u behinger exclude a single computer/host from running a job qsub -l mem=5G,h=!computername.domain script.sh to exclude multiple hosts: h=!h4&!h5 or h=!(h4|h5) Source of course mem=5G is…

This is a function I wrote back in 2014. I think it illustrates an advanced functionality in matlab that I hadn’t found written about before. The problem: Calculate the winsorized mean of a multidimensional matrix over an arbitrary dimension. Winsorized Mean The benefits of the winsorized mean can be seen here: We replace the top 10% and bottom 10% by the remaining most extreme value before calculating the mean (left panel). The right panel shows how the mean is influenced by a single outlier, but the winsorized mean is not (ignore the “yuen”-box”) Current Implementation I adapted an implementation from…