[Eeglablist] PSD on scored PSG data

Makoto Miyakoshi mmiyakoshi at ucsd.edu
Tue Jan 15 19:22:57 PST 2019


Dear Mohith,

> However, my sleep stages occur multiple times over the continuous EEG
data such as stage 1 occurs between 500 and 5000 sec, 90000 and 2700000 sec
etc.

Ok.

> Is there a way to group these EEG segments as a continuous block before I
run PSD? These segments are labelled with the sleep stage name in the field
 EEG.event.type. I very much appreciate your help :)

You can do it using EEGLAB GUI. Edit -> Select data -> [Enter the event
latency]... so that you chop up your long data into multiple pieces. Then
you can 'append data' to construct a same-condition-concatenated data.

In theory you can do the same after time-frequency transformation, but no
GUI support for this path.

Makoto

On Tue, Jan 15, 2019 at 3:56 AM VARMA, MOHITH MUKUND <
mohith96 at connect.hku.hk> wrote:

> Dear Makoto,
>
> Thank you so much, now it makes sense! However, my sleep stages occur
> multiple times over the continuous EEG data such as stage 1 occurs between
> 500 and 5000 sec, 90000 and 2700000 sec etc. Is there a way to group these
> EEG segments as a continuous block before I run PSD? These segments are
> labelled with the sleep stage name in the field  EEG.event.type. I very
> much appreciate your help :)
>
> Regards,
>
> On Tue, Jan 15, 2019 at 8:38 AM Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
> wrote:
>
>> Dear Mohith,
>>
>> > Thank you for sharing the code but perhaps I am not making myself clear
>> enough that for the PSG data that I acquired and which have been event
>> coded into different stages of sleep I am required to run a frequency
>> domain analysis of visualizing as well as obtaining the mean power spectrum
>> for specific frequency bands like alpha, delta, theta etc FOR EACH SLEEP
>> STAGE. So running a spectrogram may not be the appropriate way to visualize
>> how channel or component activity between participants differ in terms of
>> power spectra within each sleep stage (e.g. REM vs. NREM 1 vs. NREM2 VS.
>> NREM3).
>>
>> The obtained spectrogram can be also segmented into blocks, if you have
>> annotations, and you can take a block average.
>>
>> > According to what I read upon this topic, MATLAB has functions like
>> pwelch or fft to run such sort of analysis and in EEGLAB, I think its
>> pop_spectopo instead of spectrogram (since I don't need info about the time
>> domain at least for this part of the analysis), please correct me if I am
>> wrong.
>>
>> In the example below, you can see the PSD time in 'times' and freqs in
>> 'freqs'.
>>  [~, freqs, times, PSD]      = spectrogram(EEG.icaact(icIdx,:),
>> EEG.srate, round(EEG.srate/2), freqBins, EEG.srate);
>> If you know your Stage 1 is from 568.9 s to 668.2 sec,
>> startIdx = find(times>568.9,1);
>> endIdx = find(times>668.2,1)-1;
>> stage1MeanPower = squeeze(mean(PSD(:, startIdx:endIdx, icIdx), 2));
>> to obtain the Stage1 PSD (of a given independent component by icIdx).
>>
>> Makoto
>>
>>
>>
>> On Sat, Jan 12, 2019 at 7:21 PM Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
>> wrote:
>>
>>> 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
>>>>>> To unsubscribe, send an empty email to
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>>>>>> 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
>>>>>
>>>>
>>>>
>>>> --
>>>> 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
>>>
>>
>>
>> --
>> Makoto Miyakoshi
>> Swartz Center for Computational Neuroscience
>> Institute for Neural Computation, University of California San Diego
>>
>
>
> --
> 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
>


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