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<p style="margin-top:0; margin-bottom:0">Hi Makoto,</p>
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<p style="margin-top:0; margin-bottom:0">Thank you for the really great advice! The two links you provided are extremely helpful.</p>
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<p style="margin-top:0; margin-bottom:0">I had a few follow-up questions:</p>
<p style="margin-top:0; margin-bottom:0">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? </p>
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<p style="margin-top:0; margin-bottom:0">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?</p>
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<p style="margin-top:0; margin-bottom:0">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 <span style="font-family: Calibri, Helvetica, sans-serif, Helvetica, EmojiFont, "Apple Color Emoji", "Segoe UI Emoji", NotoColorEmoji, "Segoe UI Symbol", "Android Emoji", EmojiSymbols; font-size: 16px;">
(which I have already pre-processed and applied ICA on) </span>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? <span style="font-size: 12pt;">(I apologize for the potential triviality of this one!)</span></p>
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<p style="margin-top:0; margin-bottom:0">Thank you again in advance for your time and help!</p>
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<p style="margin-top:0; margin-bottom:0">Best,</p>
<p style="margin-top:0; margin-bottom:0">Panos</p>
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<p style="margin-top:0; margin-bottom:0">Panagiotis Fotiadis</p>
<p style="margin-top:0; margin-bottom:0">PhD Student | Neuroscience Graduate Group</p>
<p style="margin-top:0; margin-bottom:0"><span style="font-size:12pt">Perelman School of Medicine, University of Pennsylvania</span><br>
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<div id="divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif" color="#000000" style="font-size:11pt"><b>From:</b> Makoto Miyakoshi <mmiyakoshi@ucsd.edu><br>
<b>Sent:</b> Monday, January 7, 2019 2:48:37 PM<br>
<b>To:</b> Fotiadis, Panagiotis<br>
<b>Cc:</b> eeglablist@sccn.ucsd.edu<br>
<b>Subject:</b> [External] Re: [Eeglablist] Frequency-time spectrogram deconstruction</font>
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<div dir="ltr">Dear Panos,<br>
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Welcome to the time-frequency world.<br>
<br>
> 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?<br>
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<div>That's one way to go. Nothing is wrong with that!</div>
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<div>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</div>
<div><a href="https://sccn.ucsd.edu/mediawiki/images/a/a6/C2_A3_Time-frequencyDecAndAdvancedICAPracticum_updateJan2017.pdf" id="LPlnk215353" class="OWAAutoLink" previewremoved="true">https://sccn.ucsd.edu/mediawiki/images/a/a6/C2_A3_Time-frequencyDecAndAdvancedICAPracticum_updateJan2017.pdf</a><br>
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<div>You can also obtain bin-mean values from power spectral density. See below. </div>
<div><a href="https://sccn.ucsd.edu/wiki/Makoto's_useful_EEGLAB_code#How_to_extract_EEG_power_of_frequency_bands" id="LPlnk70051" class="OWAAutoLink" previewremoved="true">https://sccn.ucsd.edu/wiki/Makoto's_useful_EEGLAB_code#How_to_extract_EEG_power_of_frequency_bands</a><br>
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<div>Makoto</div>
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<div class="x_gmail_quote">
<div dir="ltr">On Mon, Jan 7, 2019 at 1:34 AM Fotiadis, Panagiotis <<a href="mailto:Panagiotis.Fotiadis@pennmedicine.upenn.edu" id="LPlnk798308" class="OWAAutoLink" previewremoved="true">Panagiotis.Fotiadis@pennmedicine.upenn.edu</a>> wrote:<br>
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<p style="margin-top:0px; margin-bottom:0px">Hello,</p>
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<p style="margin-top:0px; margin-bottom:0px">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:</p>
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<p style="margin-top:0px; margin-bottom:0px">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. </p>
<p style="margin-top:0px; margin-bottom:0px"><br>
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<p style="margin-top:0px; margin-bottom:0px">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.</p>
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<p style="margin-top:0px; margin-bottom:0px">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?</p>
<p style="margin-top:0px; margin-bottom:0px"><br>
</p>
<p style="margin-top:0px; margin-bottom:0px">Thank you in advance!</p>
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<p style="margin-top:0px; margin-bottom:0px">Best,</p>
<p style="margin-top:0px; margin-bottom:0px">Panos</p>
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-- <br>
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<div dir="ltr">Makoto Miyakoshi<br>
Swartz Center for Computational Neuroscience<br>
Institute for Neural Computation, University of California San Diego<br>
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