Hi Mike,

maybe my question is stupid but I am still new in this topic so I have to learn a lot.

I am trying to understand wavelet decomposition using the example data in sampleEEGData.mat that you provided in your course focusing for example on electrode 01. I started looking at the power spectrum using pwelch function as you have done.

As expected, the spectrum has a peak around 10 Hz and a flat pattern at higher frequencies.

I tried to verify if the wavelet decomposition leads to a similar result using as number of cycles 10 or 4.

As you can see, I have this strange high power at high frequencies that itâ€™s quite unexpected. The situation is confirmed if I average over time my matrix to obtain like a â€śspectrumâ€ť.

My question is: why there is strange positive trend at gamma frequencies?

Thanks for your help,

Matteo

ok, sorry for the continuous repost but I have understood something. I have this strange trend if I use a fixed number of cycles (4 o 10), whereas if I use a variable number of cycles ranging from 4 to 15 in my frequency range 1-35 Hz, I have a better pattern.

Why does it happen? So, is wrong using a fixed scale for a span of frequencies? I am a bit confused.

Thanks again,

Matteo

Hi Matteo. I guess this is raw power and not normalized (e.g., to a pre-stim period) power? With a constant number of cycles, the amount of energy that passes through convolution changes with frequency. Normalization is always a tricky issue. When possible, itâ€™s best to normalize according to an appropriate baseline period. That should eliminate these effects.

You could also run convolution to higher frequencies to see if this persists. I think the sampling rate of that dataset is 256 Hz, so you could extract frequencies up 128 Hz. That would give some hints about whether the high-frequency power reflects an issue of differences in total energy vs. gamma-band activity.

Mike

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Hi Mike,

yes itâ€™s raw. In my dataset I have resting state EEG so I would like to know how to tackle this tricky thing. What can you suggest to me?

Thank you,

Matteo