That's very steep! Can you share some plots of the spectra and the fits, as well as the fitting settings?

Hi, apologies for the delayed response. I noticed that applying a bandpass filter of 1-100Hz during analysis results in higher exponent values compared to using no bandpass filter. I tested this across different files and observed the same pattern. Below are the results from the brainstorm FOOOF algorithm using the default settings as mentioned here:https://neuroimage.usc.edu/brainstorm/Tutorials/Fooof

plots aren't in a very good state, nevertheless , here is one for your reference :

Hey @kshtjkumar - I'm not sure what's going on here, but there is something wrong with the plots - one of the lines (B-000) seems to be in a different spacing than the others, so we can't really see anything about the models.

A pretty key point here is what frequency range you are parameterizing - animal model data can be pretty steep in the high frequency ranges (eg 50-150 Hz), but I'm not clear if that's what you are doing here. You want to specify the range, make sure you avoid any ranges with a filter, and then plot the models / check the goodness of fit parameters and see how things look. I don't use Matlab, so I can't provide any guides on that aspect - but hopefully the Brainstorm documentation has some information on this

Hi @TomDonoghue, thanks for your response. I'm actually working with LFP data, bandpass filtered at 0.5-100Hz. I initially used Python, but it takes a significant amount of time to analyze even a short time range, like 30 seconds. My recordings, however, are over 60 minutes long. On the other hand, MATLAB Brainstorm processes the data in just a few minutes.

Hey @kshtjkumar - there is not much of a performance difference between Matlab and Python in actual computation time, the difference you are seeing there is probably due to different settings (Brainstorm might be defaulting a bit different). Make sure you are setting the frequency range and model settings to fit the models properly / efficiently.