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I'm pretty new to EEG analysis. I measured EEG on six scalp positions over night. When I plot the power of the frequencies, I get the following:

enter image description here

What I want to explain (and didn't manage to) is the origin of the 38 Hz peak in my case.

The most important steps to arrive at my graph were (using Python-MNE):

  1. band-pass filter of data (1 to 45 Hz)
  2. resampling from 512 Hz to 200 Hz

Then for every of the six channels:

  1. cut recording into consecutive segments of 2 sec duration

exclude segments if:

  1. maximum allowed voltage step of 50 µV is exceeded
  2. activity is lower than 0.5 µV
  3. maximum and minimum amplitude exceed +/- 200 µV
  4. maximum absolute difference of values in the segment > 200 µV

further:

  1. retain only segments that are ok for all electrodes
  2. randomly choose 15 such good segments (all electrodes) from every participant
  3. calculate FFT for every 2 sec time segment for every electrode and participant
  4. calculate mean of all these FFTs
  5. plot

My question is: is someone familiar with such an artifact at 38 Hz? Where could it come from? These are the things I already checked:

  • it is pronounced in every participant
  • it is pronounced in every electrode
  • eye movements are unlikely since this peak is there after filtering out horizontal eye movements via ICA
  • electrical devises are unlikely since they should peak at 50 Hz
  • it is pronounced when segments are 6 sec instead of 2 sec
  • I also used a notch filter at 50 Hz instead of the bandpass filter, with the following result (here the individual 15 segments are shown, but the 38 Hz peak is still there):

enter image description here

Does someone have an idea where this feature could come from?

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Someone taught me how to make a more informative plot without preprocessing the data. It thus seems like this artifact is 12.5 Hz apart from 50 Hz and therefore has something to do with the line noise from nearby electrical devices. The same pattern happens at 150 Hz.

enter image description here

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