[Eeglablist] PSD of resting state data

[Makoto Miyakoshi]

Dear Kate,

> Can I access the PSD from the study GUI or is something that needs a
script?

In the case of EEGLAB 14 or before...
After clustering the ICs in STUDY, 'Plot spectra' for all the clusters from
'Edit and plot'. After plotting all the cluster spectra, close the 'Edit
and plot' window BY PRESSING OK. This creates 'STUDY.cluster(3).icaspectra'
or something like that under each cluster which you can grab for your own
statistics.

> I actually have the same question about the ICA, how can I see if there
are differences? Where can I get the data to use in SPSS?

Differences across conditions? Then you have to 'epoch' the trials
separately for each condition. This is needed regardless of whether you
perform sensor-level or ICA-decomposed comparison. If you are interested in
ICA-clustering STUDY, you may want to check out this plugin I wrote. This
supports data export to SPSS.

https://sccn.ucsd.edu/wiki/Std_erpStudio

Makoto


On Tue, Jan 22, 2019 at 11:57 AM Katherine Eskine <
eskine_katherine at wheatoncollege.edu> wrote:

> First, thanks so much for your thoughtful responses. I am going to do a
> PSD and an ICA comparison.
>
> I have a few follow up questions I am hopeful you can help me with.
>
> Can I access the PSD from the study GUI or is something that needs a
> script? To do a repeated measures analysis where can I download the data
> from?
>
>  I actually have the same question about the ICA, how can I see if there
> are differences? Where can I get the data to use in SPSS?
>
> Thanks so much for your guidance, much obliged.
>
> Best,
>
> Kate
>
> On Jan 17, 2019, at 9:25 PM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu> wrote:
>
> Dear Katherine,
>
> > How can I best determine if there are differences between the
> distributions of frequencies bans (alpha, beta, etc.)?
>
> If you know how to code, of course you can take temporal dynamics of the
> alpha, theta, etc band power into consideration in an creative way!
> However, even just a standard power spectral density (PSD) would be
> sufficient to draw a conclusion. It is simpler too.
>
> > My original thinking was to find the average alpha, beta, theta, delta
> and gamma for the pre and the post then submit them to a repeated measures
> analysis. However, I am wondering if an analysis using the components might
> provide more information?
>
> Yes, ICA results come with their corresponding scalp topography (which is
> directly generated by ICA as inverse weight matrix, EEG.icawinv), on which
> you can perform dipole fitting using EEGLAB's infinitely close to official
> plugin Dipfit.
>
> > Can I identify significant differences in ICA's between the pre and the
> postconditions and then look at the dipoles for the brain source?
>
> You can at least compare pre and post conditions.
>
> > One follow-up question, will I run into problems because the pre and
> post conditions have the same ICA components? I assume that looking for
> different power of each component will get at any before and after
> differences, but does the structure violate vector parameters?
>
> If subjects did not take the cap between pre and post conditions, then you
> can run just one ICA on the two conditions. This is much more
> straightforward. If they did take the cap, then you MUST run ICA separately
> on pre and post, then you have to use IC clustering, hoping that a given
> cluster has both pre and post of the same subject (which is not
> guaranteed...) So the interpretation will be probabilistic.
>
> Makoto
>
>
> On Thu, Jan 17, 2019 at 11:24 AM Katherine Eskine <
> eskine_katherine at wheatoncollege.edu> wrote:
>
>> Dear all,
>>
>> I have been working through a data set and would deeply appreciate some
>> advice. I have 6 minutes of resting state data before and after exposure,
>> all recorded in the same session. I would like to see if there are
>> significant differences in frequency bands before as compared to after the
>> exposure.
>>
>> The EEG during the exposure was removed and then the continuous data has
>> been post-processed and submitted to ICA analysis, where heartbeat, eye
>> blinks, and other noisy components were removed. Then the data was split
>> into the 6 minutes before and after.
>>
>> How can I best determine if there are differences between the
>> distributions of frequencies bans (alpha, beta, etc.)?
>> * bandpass filtering & plotting per band
>> * average absolute power per band, or
>> * time-frequency transform using short-term Fourier transforms or
>> wavelets
>> * should I epoch the data into 3-second intervals and proceed from there?
>>
>> My original thinking was to find the average alpha, beta, theta, delta
>> and gamma for the pre and the post then submit them to a repeated measures
>> analysis. However, I am wondering if an analysis using the components might
>> provide more information? Can I identify significant differences in ICA's
>> between the pre and the postconditions and then look at the dipoles for the
>> brain source?
>>
>> One follow-up question, will I run into problems because the pre and post
>> conditions have the same ICA components? I assume that looking for
>> different power of each component will get at any before and after
>> differences, but does the structure violate vector parameters?
>>
>> Thanks so much for your help. I have been following the discussion from
>> Mohith, but I think my continuous data might be a slightly different case.
>>
>> Best,
>>
>> Kate
>>
>>
>> Katherine E. Eskine
>> Assistant Professor of Psychology
>> Mars SC 1136 / t. 508-286-3636
>> Wheaton College
>>
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>
>
>
> --
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>
>

-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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