[Eeglablist] topoplots of condition differences in STUDY

Stephen Politzer-Ahles politzerahless at gmail.com
Sun Aug 6 12:41:26 PDT 2017

I'm not sure exactly how it's done in STUDY (others on the list may be able
to provide more information), but for what it's worth, using STUDY is not
necessary. Basic EEGLAB has a topoplot() function which only needs a list
of amplitudes (one per channel) and a list of channel locations (the
`EEG.chanlocs` structure that is in the matlab workspace after you import a
data file and its channel locations). Once you have this list of amplitudes
(i.e., condition differences) per channel, then you can use the topoplot()
function directly; there are examples in the documentation for this
function and you can find more examples in previous messages on this list.
There are lots of different ways you could get this list; STUDY is one way,
what I usually do is write a MATLAB `for` loop to open and process each
participant's file and then combine them into a grand average, or you can
even make the list in some other software. (E.g., if you have already
processed your data, then in whatever software you use for doing statistics
you presumably already have this list of condition averages at each channel
in the time window you're interested in; all you need to do is, at each
channel, subtract one condition from the other, and then get that list of
differences into MATLAB as an array which you can use in the topoplot()


Stephen Politzer-Ahles
The Hong Kong Polytechnic University
Department of Chinese and Bilingual Studies

On Fri, Aug 4, 2017 at 5:21 PM, Guannan (Mandy) Shen <mandy.g.shen at gmail.com
> wrote:

> Dear EEG list,
> I'm working on creating some topoplots of condition differences (ERPs). I
> understand it requires some scripting in STUDY. I would really appreciate
> if someone can share your script with me!
> So far I can only generate topo-maps of p- values of condition comparison
> in STUDY using the GUI. I'm trying to generate topo-maps of actual voltage
> differences between conditions (averaged).
> Any insights would be greatly appreciated!
> Many thanks,
> Guannan Shen
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