On Wed, Aug 24, 2011 at 11:04 PM, Urs Bachofner <[log in to unmask]> wrote:
> Dear Vladimir,
> This is a response to an E-mail you sent me days ago.
> Can you please give me an explanation why the first output one gets after
> the inversion is a graph that contains the activation of the SINGLE most
> active vertex respective to the trial time?
It's just a diagnostic to see that that the inversion ran OK. You can
access any voxel and any time using the MIP button.
> My first question is: Wouldn't people using SPM for source reconstruction
> rather appreciate a graph that shows global field power over all channels to
> see the whole activation of the brain, to get to see peaks in the global
No I don't think they would because people do source reconstruction to
get some regional specificity whereas global field power does not have
any and is very similar to what you would get at the sensor level
without any source reconstruction.
> My second question: In analyzing ERP's, I am usually encountered with a
> trial drop out of 20% to 40%, mostly due to artefacts. I do get a graph from
> my own preprocessing script as I showed you before, but I would also be
> interested in such a graph after the inversion, after the bad trials have
> been removed.
> How can this be done, how can I get a graph of global field power after I
> remove all bad trials (no matter if this would be done before or after
I don't see what the problem is. You can just do something like
D = spm_eeg_load;
data = D(D.meegchannels, :, D.pickconditions(D.condlist));
to get data just from the good trials and then make your GFP plot. Or
you can use Other/Remove bad trials to only leave good trials in your
dataset and do the plot then.
> Am I really the only one who would need this kind of graph or is this
> something that should be included in a further version of SPM? Can this
> already be done in the currect version?
Since you are the only person who ever asked for this I don't think
it'll be useful for most people but let me know if you need any
further help to do it yourself.
> Thank you once more for your help which is highly appreciated.
> Urs Bachofner
> Am 19.08.2011 16:01, schrieb Vladimir Litvak:
>> Dear Urs,
>> I don't think the two figures are really comparable. One is global
>> field power summarized over all channels so you could never get a
>> negative peak in it. The other is source waveform from a SINGLE most
>> active vertex. I would expect that if you compute global field power
>> over all vertices there should be some similarity but it's now what's
>> displayed there.
>> On Fri, Aug 19, 2011 at 2:53 PM, Urs Bachofner<[log in to unmask]>
>>> Hi Vladimir,
>>> I have one final question to be answered before I create my Nifti Images.
>>> the attachment, in picture 1, you'll find a screenshot of two pictures.
>>> The first picture is the average signal over all my trials that I get
>>> filtered and epoched)
>>> The second picture is the graphics I get when Inversion is completed.
>>> data is filtered, epoched, baseline-corrected, rereferenced to average
>>> bad trials are removed).
>>> There are several differences between those two pictures and I hope you
>>> help me explaining them. Of course, in this dataset 120 of 270 trials
>>> been marked as bad. Still, is it possible that this results in such huge
>>> differences in (1) the peak time of the EEG components, (2) the power of
>>> peaks relative to one another, (3) the polarity of the peaks, which all
>>> of a
>>> sudden are negative, also?
>>> Do I misunderstand this graphic after the inverse solution? Will I have
>>> define the peaks I'm interested in based on this graphic? Where did my
>>> extremely prominent P200 go all of a sudden?
>>> To explore this further, I ran the whole Inversion again by including ALL
>>> trials, like it's done in my graphic. Please have a look at Screenshot 2
>>> the attachment.
>>> Again, the results are very different. Can you explain what the graphic
>>> shown after inversion actually means?
>>> Urs Bachofner
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