If you just want the PAF or CoG measurements, check out a 'modern'
perspective (using a very old technique for spectra from chemistry,
Savitzky-Golay filtering):
Corcoran, A. W., Alday, P. M., Schlesewsky, M., & Bornkessel-Schlesewsky,
I. (2018). Toward a reliable, automated method of individual alpha
frequency (IAF) quantification. Psychophysiology, e13064.
doi:10.1111/psyp.13064
implemented on top of MNE as part of the philistine package:
philistine · PyPI
Extending Marijn's example:
from philistine.mne import savgol_iaf
help(savgol_iaf)
savgol_iaf(raw)
The Q-factor weighting in the paper isn't yet implemented in Python, so
all channels are weighted equally.
Hi Jasmina,
plotting the frequency spectrum, averaged across all electrodes and conditions, is really easy. When you load the data you have a Raw object, for example:
raw = mne.io.read_raw(?) # the actual function you need to call depends on the dataformat you are using (.fif, .edf, .bdf, something else?)
Then, you can just do:
raw.plot_psd()
Boom! Frequency spectrum 
best,
Marijn.
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Hi everyone,
I am a relative beginner with MNE (and EEG) and I don't know what's the recommended way to analyse the alpha band.
So, I would like to know if it is recommended to determine the frequency bands of interest for each subject, by highlighting the INDIVIDUAL ALPHA FREQUENCY PEAK (IAF, ref Klimesch, 1999).
If it is the case, how can I do that? Maybe trought a standard FFT procedure, with the main of define the frequency bin having a maximum power density averaged over all electreodes and conditions in the range from 6 to 14 Hz, in line with the guidelines by Klimesch (1999) ?
Any suggestion are apprecieted!
Thank you !!!
Sincerly,
Jasmina
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