[Mne_analysis] IRASA Python version?

[Phillip Alday]

I know of no such implementation, but I've opened an issue on Github.

A simpler procedure for PSD that is often effective for removing 1/f is:

1. log transform psd and f. Then psd ~ 1 / f implies log psd ~ -log(f)
2. Fit a regression line and subtract out the fitted values / take the
residuals.
3. Back transform the residuals.

So something like (may need tweaking depending on whether data is
Epochs/Evoked/etc.):

from mne.time_frequency import psd_welch
from sklearn.linear_model import LinearRegression

psds, freqs = psd_welch(data)
line = LinearRegression()
line.fit(np.log(freqs).reshape(-1,1), psd.reshape(-1,1))
pink_noise = line.predict(np.log(freqs).reshape(-1,1))
de_pinked_log = np.log(psd).reshape(-1,1) - pink_noise
psd_depinked = np.exp( de_pinked_log.reshape(-1))


On 6/1/20 4:51 pm, Harish Gunasekaran wrote:
>         External Email - Use Caution        
> 
> Hi all,
> Does anybody implemented the IRASA (Irregular Resampling Auto-Spectral
> Analysis) method or something similar in Python? I'm interested in
> finding the neural oscillations at lower frequency bands, however, due
> to the 1/f problem it is not possible to locate the peaks at low
> frequency bands especially at the delta ranges by using the welch or
> multitaper PSD estimation methods. Does MNE Python has any advanced
> methods to overcome this problem?  
> 
> Thanks in advance,
> Harish
> 
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