<div dir="ltr">Dear Iryna,<div><br></div><div>I'm disappearing from the list until November, so this is my last response. Another lab member will answer the questions in other months.</div><div>First of all, I noticed several issues in your parameter settings. I recommend you add the following optional inputs.</div><div><br></div><div>'cycles', [3 8], 'freqs', [1 50], 'nfreqs', 50, 'timesout', 10954</div><div><br></div><div>The parameter 'timesout' was calculated as (418384+647616+32768-3342)/100, which means 'Give me data point for every 100 ms given that the sliding window length is 3342 ms. In the current parameter setting, you have data only this much</div><div><br></div><div><span style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">> Generating 200 time points (418384.0 to 647616.0 ms)</span><br style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial"><br></div><div>About the main baseline problem, I tried to replicate your problem. Look at my output log below, particularly the highlighted part.</div><div>Because my continuous data (naturally) start from latency 0, the output log says 'from 0 ms before to XXX ms after the timelocking event.'</div><div>But your log says 'from 402000 ms before to 664000 ms after the timelocking event.' I think this is related to the problem.</div><div>Does your continuous data start from latency zero? Did you try to 'epoch' that continuous data? I recommend you prepare the continuous data so that it starts from latency 0. Also, in EEGLAB, continuous data cannot be handled as single-epoched data (I don't know why...)</div><div><br></div><div>Makoto</div><div><br></div><div><br></div><div><div>> figure; pop_newtimef( EEG, 0, 1, [0 1684998], [3 8] , 'topovec', EEG.icawinv(:,1),...</div><div> 'elocs', EEG.chanlocs, 'chaninfo', EEG.chaninfo, 'caption', ['IC 1'], 'baseline',[0 3000],...</div><div> 'nfreqs', 50, 'freqs', [1 50], 'plotphase', 'off', 'padratio', 1);</div><div>Warning: 'nfreqs' input overwrite 'padratio'</div><div>Computing Event-Related Spectral Perturbation (ERSP) and</div><div> Inter-Trial Phase Coherence (ITC) images based on 1 trials</div><div> of 842499 frames sampled at 500 Hz.</div><div><span style="background-color:rgb(244,204,204)">Each trial contains samples from 0 ms before to</span></div><div><span style="background-color:rgb(244,204,204)"> 1684998 ms after the timelocking event.</span></div><div> Image frequency direction: normal</div><div>Using 3 cycles at lowest frequency to 8 at highest.</div><div>Generating 200 time points (1671.0 to 1683327.0 ms)</div><div>Finding closest points for time variable</div><div>Time values for time/freq decomposition is not perfectly uniformly distributed</div><div>The window size used is 1671 samples (3342 ms) wide.</div><div>Estimating 50 linear-spaced frequencies from 1.0 Hz to 50.0 Hz.</div><div>Processing time point (of 200): 10 20 30 40 50 60 70 80 90 100 110 120</div><div> 130 140 150 160 170 180 190 200</div><div>Computing the mean baseline spectrum</div><div>Warning: When plotting pvalues in totoplot, use option 'conv' to minimize extrapolation effects</div><div>Note: Add output variables to command line call in history to</div><div> retrieve results and use the tftopo function to replot them</div></div><br><div class="gmail_quote"><div dir="ltr">On Wed, Aug 1, 2018 at 9:31 AM Gumenchuk, Iryna <<a href="mailto:Iryna_Gumenchuk@nymc.edu">Iryna_Gumenchuk@nymc.edu</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">Permutation analysis will use data in (pre-0) baseline subwindows only.<br>
Computing Event-Related Spectral Perturbation (ERSP) and<br>
Inter-Trial Phase Coherence (ITC) images based on 1 trials<br>
of 131001 frames sampled at 500 Hz.<br><span style="background-color:rgb(244,204,204)">
Each trial contains samples from 402000 ms before to<br>
664000 ms after the timelocking event.</span><br>
Only significant values (permutation statistics p<0.01) will be colored;<br>
non-significant values will be plotted in green<br>
Image frequency direction: normal<br>
Adjust min freq. to 6.01 Hz to match FFT output frequencies<br>
Adjust max freq. to 49.99 Hz to match FFT output frequencies<br>
Using hanning FFT tapering<br>
Generating 200 time points (418384.0 to 647616.0 ms)<br>
Finding closest points for time variable<br>
Time values for time/freq decomposition is not perfectly uniformly distributed<br>
The window size used is 16384 samples (32768 ms) wide.<br>
Estimating 1442 linear-spaced frequencies from 6.0 Hz to 50.0 Hz.<br>
finding closest frequencies: 6750 freqs removed<br>
<br>
Then when we use alternate settings to specify window length based on our sampling rate (which was 500Hz), we get:<br>
<br>
same error (see attachment )<br>
<br>
<br>
________________________________<br>
From: Makoto Miyakoshi [<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>]<br>
Sent: Tuesday, July 31, 2018 3:29 PM<br>
To: Gumenchuk, Iryna; EEGLAB List<br>
Subject: Re: [Eeglablist] Time- frequency analysis<br>
<br>
Dear Iryna,<br>
<br>
Set [250] or [251] instead of [0] in the 'sub epoch...'<br>
Also, what is the exact sliding window width? Please copy and paste all the the output report that shows up in the command line. It shows the window width. [250] was just an example.<br>
<br>
Makoto<br>
<br>
On Tue, Jul 31, 2018 at 11:52 AM Gumenchuk, Iryna <<a href="mailto:Iryna_Gumenchuk@nymc.edu" target="_blank">Iryna_Gumenchuk@nymc.edu</a><mailto:<a href="mailto:Iryna_Gumenchuk@nymc.edu" target="_blank">Iryna_Gumenchuk@nymc.edu</a>>> wrote:<br>
Hi Makoto,<br>
<br>
Still doesn't work. I attached two images: one, where I put all setting, and second - the error message. May be it will give you some idea.<br>
Thank you!<br>
<br>
-I<br>
________________________________<br>
From: Makoto Miyakoshi [<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a><mailto:<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>>]<br>
Sent: Tuesday, July 31, 2018 2:24 PM<br>
To: Gumenchuk, Iryna; EEGLAB List<br>
Subject: Re: [Eeglablist] Time- frequency analysis<br>
<br>
Dear Iryna,<br>
<br>
> When I do disable baseline subtraction, then it works fine<br>
<br>
Ok so the issue is in using the baseline option.<br>
<br>
> e.g., [0 30000], I get the foll0wing error message:<br>
<br>
Let's try this together.<br>
If your sliding window length is 500 datapoint long, try<br>
<br>
[500 30000]<br>
<br>
It should work. It may also work<br>
<br>
[250 30000]<br>
<br>
or<br>
<br>
[251 30000]<br>
<br>
I think at least one of the above parameter choices should work, if I am guessing the cause of the problem correctly. Basically, your time-freq data will start from [250] or [251] which is the half length of your sliding window and no data before that.<br>
<br>
Makoto<br>
<br>
<br>
On Mon, Jul 30, 2018 at 7:36 AM Gumenchuk, Iryna <<a href="mailto:Iryna_Gumenchuk@nymc.edu" target="_blank">Iryna_Gumenchuk@nymc.edu</a><mailto:<a href="mailto:Iryna_Gumenchuk@nymc.edu" target="_blank">Iryna_Gumenchuk@nymc.edu</a>><mailto:<a href="mailto:Iryna_Gumenchuk@nymc.edu" target="_blank">Iryna_Gumenchuk@nymc.edu</a><mailto:<a href="mailto:Iryna_Gumenchuk@nymc.edu" target="_blank">Iryna_Gumenchuk@nymc.edu</a>>>> wrote:<br>
Thank you for your response!<br>
Yes, this large value is for continuous data, and I want to subtract the baseline and then apply time - frequency analysis on continuous data. When I do disable baseline subtraction, then it works fine, but I need specifically to subtract the baseline.<br>
Best,<br>
Iryna<br>
________________________________<br>
From: Makoto Miyakoshi [<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a><mailto:<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>><mailto:<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a><mailto:<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>>>]<br>
Sent: Friday, July 27, 2018 9:28 PM<br>
To: Gumenchuk, Iryna<br>
Cc: EEGLAB List<br>
Subject: Re: [Eeglablist] Time- frequency analysis<br>
<br>
Dear Iryna,<br>
<br>
> When I use the eeglab, [0 1095000] is the value that is selected by default in the time-frequency dialog (pop_newtimef()).<br>
<br>
Hmm that seems a very large value, whatever it is. What is that value? My guess is you tried to apply time-frequency analysis on continuous data, but is that what you wanted to do?<br>
<br>
Also, try 'baseline', NaN to disable baseline subtraction. What do you see?<br>
<br>
Makoto<br>
<br>
On Mon, Jul 23, 2018 at 1:53 PM Gumenchuk, Iryna <<a href="mailto:Iryna_Gumenchuk@nymc.edu" target="_blank">Iryna_Gumenchuk@nymc.edu</a><mailto:<a href="mailto:Iryna_Gumenchuk@nymc.edu" target="_blank">Iryna_Gumenchuk@nymc.edu</a>><mailto:<a href="mailto:Iryna_Gumenchuk@nymc.edu" target="_blank">Iryna_Gumenchuk@nymc.edu</a><mailto:<a href="mailto:Iryna_Gumenchuk@nymc.edu" target="_blank">Iryna_Gumenchuk@nymc.edu</a>>><mailto:<a href="mailto:Iryna_Gumenchuk@nymc.edu" target="_blank">Iryna_Gumenchuk@nymc.edu</a><mailto:<a href="mailto:Iryna_Gumenchuk@nymc.edu" target="_blank">Iryna_Gumenchuk@nymc.edu</a>><mailto:<a href="mailto:Iryna_Gumenchuk@nymc.edu" target="_blank">Iryna_Gumenchuk@nymc.edu</a><mailto:<a href="mailto:Iryna_Gumenchuk@nymc.edu" target="_blank">Iryna_Gumenchuk@nymc.edu</a>>>>> wrote:<br>
When I use the eeglab, [0 1095000] is the value that is selected by default in the time-frequency dialog (pop_newtimef()). In the<br>
case of my continuous data, when I change select values for my baseline,<br>
e.g., [0 30000], I get the foll0wing error message:<br>
There are no sample points found in the default baseline. This may happen<br>
even though data time limits overlap with the baseline period (because of<br>
the time-freq. window width). Either disable the baseline, change the<br>
baseline limits.<br>
Please advice!<br>
_______________________________________________<br>
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<br>
<br>
--<br>
Makoto Miyakoshi<br>
Swartz Center for Computational Neuroscience<br>
Institute for Neural Computation, University of California San Diego<br>
<br>
<br>
--<br>
Makoto Miyakoshi<br>
Swartz Center for Computational Neuroscience<br>
Institute for Neural Computation, University of California San Diego<br>
<br>
<br>
--<br>
Makoto Miyakoshi<br>
Swartz Center for Computational Neuroscience<br>
Institute for Neural Computation, University of California San Diego<br>
</blockquote></div><br clear="all"><div><br></div>-- <br><div dir="ltr" class="gmail_signature"><div dir="ltr">Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<br></div></div></div>