= 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 [[https://fooof-tools.github.io/fooof/|FOOOF GitHub]].<<BR>><<BR>> {{attachment:FOOOF_schematic.png||height="433",width="600"}}

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]].

== 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'''".