[Eeglablist] PSD of resting state data

[Makoto Miyakoshi]

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
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