# Guide For The Selection of cwt_n_cycles {Disarmed}

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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

https://secure-web.cisco.com/1msBx4Jit68_nfxMYTAGESaHHM4kxDy0ef1RW3L664o6Fd1qMQeJ4fmGiXMyUfYyoclcrv6xDvOEf99ssSVB4o14QlKyNjUFknSDZXBhV8CMjo0V5en5FeBkaeaOOzSPh5RkBVATCYyY-mHtgY1qXKNqYHMB6EyZis8KS2wbM3ayjbo0ZaAu2DzVyNfaMovFDGbjTwkoixtG6OrNGPk4ed-EHoLjJ2Dsgy-fF3mPKePsymJinD89xd63nY6fntXfQfNDj1F9BX4fp3Uf36KkMAA/https%3A%2F%2Fmne.tools%2Fstable%2Fauto_examples%2Fconnectivity%2Fplot_cwt_sensor_connecti
vity.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

Thanks

Regards

Rodney

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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