[Eeglablist] PSD of resting state data
Tarik S Bel-Bahar
tarikbelbahar at gmail.com
Thu Jan 17 13:10:37 PST 2019
Hi Katherine, notes below, good luck with this process, thanks for sharing
back if you can with any learnings or insights along the EEG path.
- Looks like your processing path is okay (I'll assume no channel
interpolation before ICA). 6 minutes after cleaning is plenty of time for
spectral estimates, even 2 minutes would do.
- Your first more straightforward bet is to cut into 3 sec epochs and
get an estimate of power at each band. This can be done with spectopo
function. It is usual to cut continuous data into epochs for spectral power
estimates, which are usually averaged across some number of epochs.
- You could do the same for time-frequency in eeglab (Check out chronux
functions or consider building from Mike X. Cohen's excellent book and
matlab codes. If doing time-frequency, you pull estimates across time, or
at particular points in time, and can also look for brief temporal effects
if of interest (e.g., alpha bursts).
- Your repeated measures analysis sounds normal, double check with a few
publications in high-quality journals for best methods, and see papers on
ERP/EEG guidelines and stats (such as from Steve Luck and recent issues
focused on stats in Psychophysiology)
- Regarding using components that's fine, and some researchers focus on
ICs instead of the channel-level data, for multiple good reasons, one of
them being that ICA isolates different kinds of brain dynamics and reduces
the analysis space from X number of channels to a more manageable handful
of neural ICs (aka sources).
- In using ICs, one would take spectral estimates from the ICA
activation time-series. Some things to think about are that ICs tend to
have peaks at alpha or theta, and estimates of other bands may be weak or
invalid when getting multi-band estimates from one IC. Another thing to
think about is that not every subject results in the same set of basic
neural ICs, and that you'll likely have to focus on
On Thu, Jan 17, 2019 at 2:22 PM 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
> 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.
> Katherine E. Eskine
> Assistant Professor of Psychology
> Mars SC 1136 / t. 508-286-3636
> Wheaton College
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