<p><span style="padding: 3px 10px; border-radius: 5px; color: #ffffff; font-weight: bold; display: inline-block; background-color: #ff0000;"> External Email - Use Caution </span></p><p></p><div dir="ltr"><div dir="ltr">hi,</div><br><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">Thanks for the insight, it helps refine my search on the net.<br>
<br>
I would like to confirm, say one is interested in finding the connectivity<br>
between several channels within the frequency band between 1 to 5 Hz with<br>
1Hz increment. Assume, the number of complex Morlet wavelet cycle is 3<br>
(based on your rule of thumb). Does the value 3 represent the number of<br>
cycles globally? Such that, in the spectral_connectivity module, the<br>
parameter for cwt_n_cycles is set equal to 3. <br></blockquote><div><br></div><div>yes</div><div> </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
Or something that I found unlikely (i.e., cycle for each frequency) but<br>
presented in the aforemention basic mne example. Such that, the cwt_freqs =<br>
np.arange(1, 5, 1), then the<br>
cwt_n_cycles = cwt_freqs /3?<br></blockquote><div><br></div><div>what it does here is to have n_cycles that scales linearly with the frequency.</div><div><br></div><div>note however that sensor space connectivity has been debated in the literature</div><div>as sensors do not map directly to brain locations due to field spread. Some</div><div>researchers would not endorse a paper doing sensor space connectivity.</div><div><br></div><div>Alex</div><div><br></div><div> </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
<br>
<br>
Regards<br>
Rodney<br>
<br>
----------------------------------------------------------------------<br>
<br>
Message: 1<br>
Date: Tue, 17 Nov 2020 08:53:07 +0100<br>
From: Alexandre Gramfort <<a href="mailto:alexandre.gramfort@inria.fr" target="_blank">alexandre.gramfort@inria.fr</a>><br>
Subject: Re: [Mne_analysis] Guide For The Selection of cwt_n_cycles<br>
{Disarmed}<br>
To: Discussion and support forum for the users of MNE Software<br>
<<a href="mailto:mne_analysis@nmr.mgh.harvard.edu" target="_blank">mne_analysis@nmr.mgh.harvard.edu</a>><br>
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<br>
External Email - Use Caution <br>
<br>
hi,<br>
<br>
well I can just share with you my rule of thumb. If you truly look for<br>
oscillations and not just evoked responses your time-frequency atom<br>
(wavelet) should have at least 3 cycles. The more cycles the better the<br>
frequency resolution and the worse the time resolution. So it's always a<br>
trade-off and it depends on your data and your question.<br>
<br>
HTH<br>
Alex<br>
<br>
<br>
On Tue, Nov 17, 2020 at 7:42 AM <<a href="mailto:balandongiv@gmail.com" target="_blank">balandongiv@gmail.com</a>> wrote:<br>
<br>
> External Email - Use Caution<br>
><br>
> Dear Group,<br>
><br>
><br>
><br>
> I am trying to understand the tutorial with the title: Compute <br>
> seed-based time-frequency connectivity in sensor space, which is <br>
> accessible via the link<br>
><br>
> *MailScanner has detected a possible fraud attempt from <br>
> "<a href="http://secure-web.cisco.com" rel="noreferrer" target="_blank">secure-web.cisco.com</a>" claiming to be* <br>
> <a href="https://secure-web.cisco.com/1MR6NJbbVbHaHThcDpc-5P1Ke1Z02OLPDuH0pakIY" rel="noreferrer" target="_blank">https://secure-web.cisco.com/1MR6NJbbVbHaHThcDpc-5P1Ke1Z02OLPDuH0pakIY</a><br>
> 0P_kwRec-izqBnYljKZCvNBHHaOu0zz68P7Ng4-m4M7C4RpqFMoAT-qabSggYOy1kPJpK2<br>
> SXZ0-Qsh-FHPg53t-42gH_Rn0ltMg43LCUPAHWma30bPEmFfKNhkEjlx_bJgybHWo34vMu<br>
> YzDVWtUN29AkpRKswJEWEIqlGyXw1hhk2NFgaPhUqEox7WvfUO5l7zwasDzeI0xC_wPPh6<br>
> 5c8_osFLGu77OBgi-fs_hHsdvgIQ/https%3A%2F%2Fmne.tools%2Fstable%2Fauto_e<br>
> xamples%2Fconnectivity%2Fplot_cwt_sensor_connectivity.html<br>
><br>
<<a href="https://secure-web.cisco.com/1C2D8Ha4l_YR0obIvl4YWwj-OVYq74zsi_-kmSQBMHGhWrkYsCvbnT6C3TBjML1P0jYz0iTMCmzj8VuWgAwrBl-1VZ0fSDeiknpWg4TokGeow9brn6Pvv30Ql2vShGCxTFoolf0eMuKQMUHw5cN9hmWrrALUPfbBoakcJbmYaUV0ByOW9nTwXlPP6pXTnHfXcdo-0sHZcx0zH8TQFqw2EmN4_VBeR0CZS_5yMMfaDzyjHt0iEDAKf05fW0YOyDNmiMF_3fMJQ6rnt83RTK" rel="noreferrer" target="_blank">https://secure-web.cisco.com/1C2D8Ha4l_YR0obIvl4YWwj-OVYq74zsi_-kmSQBMHGhWr<br>
kYsCvbnT6C3TBjML1P0jYz0iTMCmzj8VuWgAwrBl-1VZ0fSDeiknpWg4TokGeow9brn6Pvv30Ql2<br>
vShGCxTFoolf0eMuKQMUHw5cN9hmWrrALUPfbBoakcJbmYaUV0ByOW9nTwXlPP6pXTnHfXcdo-0s<br>
HZcx0zH8TQFqw2EmN4_VBeR0CZS_5yMMfaDzyjHt0iEDAKf05fW0YOyDNmiMF_3fMJQ6rnt83RTK</a><br>
t_19w/https%3A%2F%2Fmne.tools%2Fstable%2Fauto_examples%2Fconnectivity%2Fplot<br>
_cwt_sensor_connectivity.html>.<br>
> The tutorial objective is to compute the connectivity between a <br>
> seed-gradiometer close to the visual cortex and all other <br>
> gradiometers. The connectivity is computed in the time-frequency <br>
> domain using Morlet wavelets, and the debiased squared weighted phase <br>
> lag index is used as a connectivity metric.<br>
><br>
><br>
><br>
> In the example, the band of interest are cwt_freqs = np.arange(7, 30, 2).<br>
> Then, the number of cycles is defined as cwt_n_cycles = cwt_freqs / <br>
> 7. May I know what is the logic/engineering explanation for the <br>
> selection of value SEVEN, here? I have searched the internet, but nothing<br>
relevant comes out.<br>
> This may be due to incorrect keyword use.<br>
><br>
><br>
><br>
> Appreciate if someone kind enough to shed some light and direct me to <br>
> good reading material about this topic.<br>
><br>
><br>
<br>
><br>
><br>
> Thanks<br>
><br>
><br>
><br>
> Regards<br>
><br>
> Rodney<br>
> _______________________________________________<br>
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