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Thanks for the insight, it helps refine my search on the net.
I would like to confirm, say one is interested in finding the connectivity
between several channels within the frequency band between 1 to 5 Hz with
1Hz increment. Assume, the number of complex Morlet wavelet cycle is 3
(based on your rule of thumb). Does the value 3 represent the number of
cycles globally? Such that, in the spectral_connectivity module, the
parameter for cwt_n_cycles is set equal to 3.
Or something that I found unlikely (i.e., cycle for each frequency) but
presented in the aforemention basic mne example. Such that, the cwt_freqs
np.arange(1, 5, 1), then the
cwt_n_cycles = cwt_freqs /3?
what it does here is to have n_cycles that scales linearly with the
note however that sensor space connectivity has been debated in the
as sensors do not map directly to brain locations due to field spread. Some
researchers would not endorse a paper doing sensor space connectivity.
Date: Tue, 17 Nov 2020 08:53:07 +0100
From: Alexandre Gramfort <alexandre.gramfort at inria.fr>
Subject: Re: [Mne_analysis] Guide For The Selection of cwt_n_cycles
To: Discussion and support forum for the users of MNE Software
<mne_analysis at nmr.mgh.harvard.edu>
2eEMNneQXzg at mail.gmail.com>
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well I can just share with you my rule of thumb. If you truly look for
oscillations and not just evoked responses your time-frequency atom
(wavelet) should have at least 3 cycles. The more cycles the better the
frequency resolution and the worse the time resolution. So it's always a
trade-off and it depends on your data and your question.
> External Email - Use Caution
> Dear Group,
> I am trying to understand the tutorial with the title: Compute
> seed-based time-frequency connectivity in sensor space, which is
> accessible via the link
> *MailScanner has detected a possible fraud attempt from
> "secure-web.cisco.com" claiming to be*
> The tutorial objective is to compute the connectivity between a
> seed-gradiometer close to the visual cortex and all other
> gradiometers. The connectivity is computed in the time-frequency
> domain using Morlet wavelets, and the debiased squared weighted phase
> lag index is used as a connectivity metric.
> In the example, the band of interest are cwt_freqs = np.arange(7, 30, 2).
> Then, the number of cycles is defined as cwt_n_cycles = cwt_freqs /
> 7. May I know what is the logic/engineering explanation for the
> selection of value SEVEN, here? I have searched the internet, but nothing
relevant comes out.
> This may be due to incorrect keyword use.
> Appreciate if someone kind enough to shed some light and direct me to
> good reading material about this topic.
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