[Eeglablist] PSD on scored PSG data
Makoto Miyakoshi
mmiyakoshi at ucsd.edu
Sat Jan 12 19:21:02 PST 2019
Dear Mohith,
Oops, I pasted a wrong one. Here is the right version, sorry for confusion.
Please discard the last one and use this one. Note the second input
arguments to spectrogram() is EEG.srate which means 1-s sliding window is
being used.
Makoto
% Define the freq bins.
freqBins = 1:0.01:40;
% Create an empty PSD tensor (time & freq & IC).
PSD_tensor = [];
% Compute the PSD tensor.
for icIdx = 1:size(EEG.icaact,1)
% Compute short-term Fourier Transform for 1-s window, 1-50 Hz, 0.1 Hz
bin.
if icIdx == 1;
[~, freqs, times, firstPSD] = spectrogram(EEG.icaact(icIdx,:),
EEG.srate, round(EEG.srate/2), freqBins, EEG.srate);
PSD_tensor = zeros(length(freqs), size(firstPSD,2),
size(EEG.icaact,1));
PSD_tensor(:,:,icIdx) = firstPSD;
else
[~, ~, ~, PSD_tensor(:,:,icIdx)] = spectrogram(EEG.icaact(icIdx,:),
EEG.srate, round(EEG.srate/2), freqBins, EEG.srate);
end
end
On Thu, Jan 10, 2019 at 6:53 PM Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
wrote:
> Dear Mohith,
>
> See my latest response to Panos (pasted below).
> In EEGLAB, your time-series data are stored under EEG.data as channels x
> time. So, if you want to use spectrogram(), it will be something like
> this...see below. This is from my recent work so it works. Note I'm working
> on EEG.icaact which is one of ICA's results. Replace it with EEG.data if
> you want to work on sensor level.
>
> Makoto
>
>
>
> % Define the freq bins.
> freqBins = 1:0.01:50;
>
> % Create an empty PSD tensor (time & freq & IC).
> PSD_tensor = [];
>
> % Compute the PSD tensor.
> for icIdx = 1:size(EEG.icaact,1)
>
> % Compute short-term Fourier Transform for 1-s window, 1-50 Hz, 0.1 Hz
> bin.
> if icIdx == 1;
> [~, freqs, times, firstPSD] =
> spectrogram(EEG.icaact(icIdx,:), ceil(EEG.xmax), [], freqBins, EEG.srate);
> PSD_tensor = zeros(length(freqs), size(firstPSD,2),
> size(EEG.icaact,1));
> PSD_tensor(:,:,icIdx) = firstPSD;
> else
> [~, ~, ~, PSD_tensor(:,:,icIdx)] =
> spectrogram(EEG.icaact(icIdx,:), ceil(EEG.xmax), [], freqBins, EEG.srate);
> end
> end
>
> Makoto
>
>
>
> On Thu, Jan 10, 2019 at 3:57 PM Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
> wrote:
>
>> Dear Panos,
>>
>> > 1) In addition to calculating the average absolute power (as your
>> script nicely shows), I was also interested in calculating the average
>> absolute (and relative) power at binned time intervals (e.g. avg power
>> between 0-1sec, avg power between 1-2sec, etc) within the dataset. I tried
>> to use the "spectra" output from spectopo but from what I gather it comes
>> up with [(sampling rate)/2 + 1] points rather that one power-spectral point
>> per timepoint. How would you recommend that I proceed?
>>
>> You can repeatedly apply EEGLAB spectopo() function to perform hand-made
>> short-term Fourier transform (STFT), but alternatively you might want to
>> use either EEGLAB newtimef() or Matlab spectrogram() function (the latter
>> may require some additional Toolbox). The output will be frequency x time
>> matrix. The interval of time bins needs to be calcualted. Basically,
>> {(length of data) - (sliding window length)}/(number of steps) gives you
>> the interval (step size). Adjust the (number of steps) so that you can
>> obtain the desired interval.
>>
>> > 2) Is there a way to display how do topographic maps (scalp heat maps)
>> change with time (I'm able to see how they change with different
>> frequencies but I was interested in seeing how they also change with time)?
>> Would the function timtopo be the best way to do that?
>>
>> See this wiki page.
>>
>> https://sccn.ucsd.edu/wiki/Chapter_02:_Writing_EEGLAB_Scripts#Creating_a_scalp_map_animation
>>
>> > 3) A more general question: If I write a matlab script that I would
>> like to apply on a bunch of datasets (which in my case are just epochs of
>> different lengths that I have extracted from my original dataset), should I
>> put all said datasets (which I have already pre-processed and applied ICA
>> on) in a STUDY set and then apply the script there, or should I just write
>> a for loop in matlab and apply the script in each individual dataset? In
>> other words, does the STUDY set offer an advantage in this case? (I
>> apologize for the potential triviality of this one!)
>>
>> If you are a beginner, it is always a good idea to make things as simple
>> as possible. I recommend you organize your own code to loop the
>> single-subject process for all the subjects. After all, that's the only to
>> learn the process!
>>
>> Makoto
>>
>> On Wed, Jan 9, 2019 at 11:47 AM Fotiadis, Panagiotis <
>> Panagiotis.Fotiadis at pennmedicine.upenn.edu> wrote:
>>
>>> Hi Makoto,
>>>
>>>
>>> Thank you for the really great advice! The two links you provided are
>>> extremely helpful.
>>>
>>>
>>> I had a few follow-up questions:
>>>
>>> 1) In addition to calculating the average absolute power (as your script
>>> nicely shows), I was also interested in calculating the average absolute
>>> (and relative) power at binned time intervals (e.g. avg power between
>>> 0-1sec, avg power between 1-2sec, etc) within the dataset. I tried to use
>>> the "spectra" output from spectopo but from what I gather it comes up with
>>> [(sampling rate)/2 + 1] points rather that one power-spectral point per
>>> timepoint. How would you recommend that I proceed?
>>>
>>>
>>> 2) Is there a way to display how do topographic maps (scalp heat maps)
>>> change with time (I'm able to see how they change with different
>>> frequencies but I was interested in seeing how they also change with time)?
>>> Would the function timtopo be the best way to do that?
>>>
>>>
>>> 3) A more general question: If I write a matlab script that I would
>>> like to apply on a bunch of datasets (which in my case are just epochs of
>>> different lengths that I have extracted from my original dataset), should I
>>> put all said datasets (which I have already pre-processed and applied ICA
>>> on) in a STUDY set and then apply the script there, or should I just write
>>> a for loop in matlab and apply the script in each individual dataset? In
>>> other words, does the STUDY set offer an advantage in this case? (I
>>> apologize for the potential triviality of this one!)
>>>
>>>
>>> Thank you again in advance for your time and help!
>>>
>>>
>>> Best,
>>>
>>> Panos
>>>
>>>
>>> Panagiotis Fotiadis
>>>
>>> PhD Student | Neuroscience Graduate Group
>>>
>>> Perelman School of Medicine, University of Pennsylvania
>>> ------------------------------
>>> *From:* Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
>>> *Sent:* Monday, January 7, 2019 2:48:37 PM
>>> *To:* Fotiadis, Panagiotis
>>> *Cc:* eeglablist at sccn.ucsd.edu
>>> *Subject:* [External] Re: [Eeglablist] Frequency-time spectrogram
>>> deconstruction
>>>
>>> Dear Panos,
>>>
>>> Welcome to the time-frequency world.
>>>
>>> > Would I just need to bandpass filter my post-processed EEG signal to
>>> each frequency range of interest (i.e., alpha: 8-12Hz etc) and then plot
>>> the remaining EEG signal over time, or is there another way to do this?
>>>
>>> That's one way to go. Nothing is wrong with that!
>>>
>>> More convenient and established way to go is to perform time-frequency
>>> transform using short-term Fourier transform or Wavelet transform. Google
>>> EEGLAB time-frequency and you'll find many of our past workshop materials.
>>> For example, see Slide 21 of this file
>>>
>>> https://sccn.ucsd.edu/mediawiki/images/a/a6/C2_A3_Time-frequencyDecAndAdvancedICAPracticum_updateJan2017.pdf
>>>
>>> You can also obtain bin-mean values from power spectral density. See
>>> below.
>>>
>>> https://sccn.ucsd.edu/wiki/Makoto's_useful_EEGLAB_code#How_to_extract_EEG_power_of_frequency_bands
>>>
>>> Makoto
>>>
>>> On Mon, Jan 7, 2019 at 1:34 AM Fotiadis, Panagiotis <
>>> Panagiotis.Fotiadis at pennmedicine.upenn.edu> wrote:
>>>
>>> Hello,
>>>
>>>
>>> I am fairly new to EEGLab and I had a question concerning the
>>> deconstruction of my EEG signal into its alpha/beta/theta/delta
>>> sub-components:
>>>
>>>
>>> After pre-processing some subjects with EEG data from 128 channels and
>>> performing ICA (using runica), I used eeglab and chronux to plot the
>>> power/frequency and frequency/time spectrograms of several epochs of
>>> interest.
>>>
>>>
>>> Is there a way to extract the alpha/beta/theta/delta frequencies of
>>> those epochs and quantify when they occur in time? I can visualize when
>>> each type of neuronal oscillation occurs by looking at the overall
>>> frequency/time spectrogram, but I was wondering whether there was a more
>>> robust way to actually plot each type of oscillation separately and/or
>>> quantify when it occurs.
>>>
>>>
>>> Would I just need to bandpass filter my post-processed EEG signal to
>>> each frequency range of interest (i.e., alpha: 8-12Hz etc) and then plot
>>> the remaining EEG signal over time, or is there another way to do this?
>>>
>>>
>>> Thank you in advance!
>>>
>>>
>>> Best,
>>>
>>> Panos
>>> _______________________________________________
>>> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
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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
>
> On Thu, Jan 10, 2019 at 6:34 PM VARMA, MOHITH MUKUND <
> mohith96 at connect.hku.hk> wrote:
>
>> Dear all,
>>
>> I am trying to conduct PSD (Power Spectrum Density) analysis for each of
>> the sleep stages across different frequency bands. I have scored the PSG
>> data (edf format) on a Python toolbox called Sleep and obtained the
>> hypnogram that I can use as time codes for each sleep stage. I imported
>> this time code file as event file (it contains info on the latency and type
>> of sleep stage) and now the thing I am stuck on is how to run PSD (possibly
>> using spectopo) for the same event type across different frequency band. I
>> think I cannot use the GUI to run this part so your help for setting up the
>> parameters in the MATLAB command would be really helpful! My data's
>> sampling rate is 250 Hz and FFT window size is 4 sec and other paramters I
>> can go with the default settings provided by EEGLAB. I hope my question is
>> clear enough, please let me know if you need further information.
>>
>> Regards,
>>
>> --
>> Mohith M. Varma (Mo)
>> Graduate Research Assistant
>>
>> Social & Cognitive Neuroscience Laboratory
>> Department of Psychology
>> Faculty of Social Sciences
>> The University of Hong Kong
>> Tel: (+852) 52622875
>> Email: mohith96 at connect.hku.hk
>> _______________________________________________
>> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
>> To unsubscribe, send an empty email to
>> eeglablist-unsubscribe at sccn.ucsd.edu
>> For digest mode, send an email with the subject "set digest mime" to
>> eeglablist-request at sccn.ucsd.edu
>
>
>
> --
> 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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