[Mne_analysis] Guide For The Selection of cwt_n_cycles {Disarmed}

[Alexandre Gramfort]

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hi,

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.

HTH
Alex


On Tue, Nov 17, 2020 at 7:42 AM <balandongiv at gmail.com> wrote:

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> 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
>
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> <https://secure-web.cisco.com/1C2D8Ha4l_YR0obIvl4YWwj-OVYq74zsi_-kmSQBMHGhWrkYsCvbnT6C3TBjML1P0jYz0iTMCmzj8VuWgAwrBl-1VZ0fSDeiknpWg4TokGeow9brn6Pvv30Ql2vShGCxTFoolf0eMuKQMUHw5cN9hmWrrALUPfbBoakcJbmYaUV0ByOW9nTwXlPP6pXTnHfXcdo-0sHZcx0zH8TQFqw2EmN4_VBeR0CZS_5yMMfaDzyjHt0iEDAKf05fW0YOyDNmiMF_3fMJQ6rnt83RTKt_19w/https%3A%2F%2Fmne.tools%2Fstable%2Fauto_examples%2Fconnectivity%2Fplot_cwt_sensor_connectivity.html>.
> 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.
>
>
>
>
>
> Thanks
>
>
>
> Regards
>
> Rodney
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