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| == Introduction == The Fitting Oscillations and One-Over-F (FOOOF) algorithm is designed to identify and model features of the neural power spectrum. It performs a sequential decomposition of the power spectrum into aperiodic and periodic components, modelling the spectrum using a least-squared-error approach. The present tutorial will demonstrate the algorithm’s functionality within the Brainstorm interface; for a detailed breakdown of the algorithm, please refer to the [[https://fooof-tools.github.io/fooof/|FOOOF GitHub]].<<BR>><<BR>> {{attachment:FOOOF_schematic.png||height="433",width="600"}} |
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| This demonstration uses sample data from the [[https://neuroimage.usc.edu/brainstorm/DatasetIntroduction|introduction dataset]], and can be followed by completing all steps of the Brainstorm tutorial up to and including [[https://neuroimage.usc.edu/brainstorm/Tutorials/ArtifactsFilter|tutorial 10]]. | |
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| The Fitting Oscillations and One-Over-F (FOOOF) algorithm is designed to identify and model features of the neural power spectrum. It performs a sequential decomposition of the power spectrum into aperiodic and periodic components, modelling the spectrum using a least-squared-error approach. The present tutorial will demonstrate the algorithm’s functionality within the Brainstorm interface; for a detailed breakdown of the algorithm, please refer to the [[https://fooof-tools.github.io/fooof/|FOOOF GitHub]]. | == Initializing and running FOOOF == FOOOF is principally performed on Welch’s PSD files. Analysis of Fourier transforms are also possible, but not recommended. We will run FOOOF over the PSD files we previously produced. * Clear the list of files in the Process1 tab. * Select the PSDs from the three datasets we have linked to our protocol and load them into Process1. You can select the two "PSD: 179/4000ms Power (MEG)" files and the single "PSD: 59/4000ms Power (MEG)". If you do not have these PSDs, refer to [[https://neuroimage.usc.edu/brainstorm/Tutorials/ArtifactsFilter|tutorial 10]]. * Click on [Run] to open the pipeline editor window. * Select the process "'''Frequency > FOOOF > Generate FOOOF models'''". <<BR>><<BR>> {{attachment:FOOOF_pipeline.png||height="170",width="250"}} * Select the following option: * '''FOOOF version''': Matlab <<BR>>This option allows you to choose which version of FOOOF you would like to run; either the MATLAB standalone version, or the original Python version. Results and computation times may vary slightly between versions but are roughly the same. * Click "'''Edit...'''" on '''FOOOF options'''. |
Fitting Oscillations and One-Over-F (FOOOF)
Author: Luc Wilson
This tutorial introduces the features developed in Brainstorm to compute and view FOOOF models from a Welch’s Power Spectral Density (PSD) file.
Introduction
The Fitting Oscillations and One-Over-F (FOOOF) algorithm is designed to identify and model features of the neural power spectrum. It performs a sequential decomposition of the power spectrum into aperiodic and periodic components, modelling the spectrum using a least-squared-error approach. The present tutorial will demonstrate the algorithm’s functionality within the Brainstorm interface; for a detailed breakdown of the algorithm, please refer to the FOOOF GitHub.
This demonstration uses sample data from the introduction dataset, and can be followed by completing all steps of the Brainstorm tutorial up to and including tutorial 10.
Initializing and running FOOOF
FOOOF is principally performed on Welch’s PSD files. Analysis of Fourier transforms are also possible, but not recommended. We will run FOOOF over the PSD files we previously produced.
- Clear the list of files in the Process1 tab.
- Select the PSDs from the three datasets we have linked to our protocol and load them into Process1.
You can select the two "PSD: 179/4000ms Power (MEG)" files and the single "PSD: 59/4000ms Power (MEG)". If you do not have these PSDs, refer to tutorial 10.
- Click on [Run] to open the pipeline editor window.
Select the process "Frequency > FOOOF > Generate FOOOF models".
- Select the following option:
FOOOF version: Matlab
This option allows you to choose which version of FOOOF you would like to run; either the MATLAB standalone version, or the original Python version. Results and computation times may vary slightly between versions but are roughly the same.
Click "Edit..." on FOOOF options.