[Eeglablist] PSD of resting state data

Fri Jan 25 17:15:57 PST 2019

Dear katherine,

> I read the note that it is very important to co-register the locations,
when setting dipfit parameters, but it isn't working.

Yes, sometimes the initial head orientation is 90 degrees rotated, so that
your Fz is registered near right ear. You definitely want to correct this.

> When I hit manually co-register I get Figure 1 (clearly not lining up),
so I click warp and get the second picture. Is there someplace I can find
how to accurately pair the EGI (E1, E2, E3...) to the correct names (F9,
F10 etc..)?

The locations are only registered to the labels of international 10-5
system (Oostenveld and Praamstra, 2001). Because EGI channels locations and
electrode labels do not match to the 10-5 system, you cannot use the label
matching and warping. You should manually coregister the channels to the
model head (if you are using a template EGI channel locations, you perform
this manual coregistration once, obtain the parameters by eegh, and apply
the set of parameters to the rest of the all subjects).

Makoto

On Wed, Jan 23, 2019 at 8:49 AM Katherine Eskine <
eskine_katherine at wheatoncollege.edu> wrote:

> Hello,
>
> I am preparing to cluster and realized that I need to go back in dipfit my
> data. I loaded channel locations (see below) for my EGI 128 sensor montage.
> I read the note that it is very important to co-register the locations,
> when setting dipfit parameters, but it isn't working. When I hit manually
> co-register I get Figure 1 (clearly not lining up), so I click warp and get
> the second picture. Is there someplace I can find how to accurately pair
> the EGI (E1, E2, E3...) to the correct names (F9, F10 etc..)? Thanks again
> for your help. I promise to finish this up and stop bothering you very soon
> ;).
>
> Best,
>
> Kate
> Katherine E. Eskine
> Assistant Professor of Psychology
> Mars SC 1136 / t. 508-286-3636
> Wheaton College
>
>
>
> On Wed, Jan 23, 2019 at 9:30 AM Katherine Eskine <
> eskine_katherine at wheatoncollege.edu> wrote:
>
>> Hello,
>>
>> Thanks so much for writing such a fantastic plug in! I am excited to give
>> that a try. I have a few quick clarification questions.
>> 1. Currently I have epoched my data into pre and post and created a study
>> with 2 conditions per subject. Is it still reasonable to cluster from this
>> juncture?
>> 2. Should I interpolate channels at the study level?
>> 3. Finally, because I wanted to maintain the continuity of the continuous
>> data I did not remove noisy epochs (time periods - I don't really have
>> epochs) in hopes that the amount of noise would be similar in the pre and
>> post conditions (just wanted to know if you had any thoughts on that).
>>
>> I am eternally grateful for this help. I've been working on these
>> problems for an embarrassingly long time, and can finally see the light at
>> the end of the tunnel. THANK YOU!
>>
>> Best,
>>
>> Kate
>>
>>
>> Katherine E. Eskine
>> Assistant Professor of Psychology
>> Mars SC 1136 / t. 508-286-3636
>> Wheaton College
>>
>>
>>
>> On Tue, Jan 22, 2019 at 4:59 PM Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
>> wrote:
>>
>>> 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
>>>
>>

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