FOOOF is a fast, efficient, and physiologically-informed tool to parameterize neural power spectra. To learn more about it, please visit the official FOOOF repository.

This EEGLAB plugin uses FOOOF's matlab wrapper, fooof_mat.

Provided FOOOF's Python dependencies are satisfied (numpy and scipy >= 0.19), install FOOOF with pip:

$ pip install fooof

or conda:

$ conda install -c conda-forge fooof

make sure Matlab's version of Python (verified with >>pyversion) is >= 3.0

• Switch versions with >> pyversion 3.X or >> pyversion ~/location_of_python

To add this plugin to EEGLAB, clone this repository into ~/MATLAB/eeglab_version/plugins

$ git clone https://github.com/bfbarry/EEGLAB-FOOOF.git

For EEG data, load a dataset (for a test set, load: ~/MATLAB/eeglab_version/sample_data/eeglab_data_epochs_ica.set)

To fit power spectra, select Tools > FOOOF > Fit power spectra. This calls pop_eeg_fooof(). The following menu will appear:

• ICs/channels to fit can be a colon vector, array, or single value.
• epoch range and percent of data to sample for computing are default pop_spectopo() arguments
• Frequency range to fit will be asked by default on every UI item in this plugin and determines both spectral computation and fitting range.
• The FOOOF settings are optional and used for the algorithm. For a thorough explanation of these, please see the wiki.
  • peak_width_limits sets the possible lower- and upper-bounds for the fitted peak widths.
  • max_n_peaks sets the maximum number of peaks to fit.
  • min_peak_height sets an absolute limit on the minimum height (above aperiodic) for any extracted peak.
  • peak_threshold sets a relative threshold above which a peak height must cross to be included in the model.
  • aperiodic_mode defines the approach to use to parameterize the aperiodic component.

Fit power spectra will add a struct to EEG.etc called FOOOF_results. Here is what indexing the first component of this struct approximately looks like:

>> EEG.etc.FOOOF_results{1}

ans = 

  struct with fields:

    aperiodic_params: [1.5325 1.4917]
         peak_params: [3×3 double]
     gaussian_params: [3×3 double]
               error: 0.0309
           r_squared: 0.9982
               freqs: [1...n]
      power_spectrum: [1×33 double]
    fooofed_spectrum: [1×33 double]
              ap_fit: [1×33 double]

To plot a component's fit, select Tools > FOOOF > Plot fit. This calls pop_eeg_fooofplot(). The following menu will appear:

This has the same arguments as before, but will only accept one IC or channel to plot. You may also plot it on log-log axes.

For STUDY data, load a study (for a test study, download the STERN study, and once loaded go to STUDY > Precompute component measures > and select power spectrum to extract necessary files.)

To fit power spectra, select STUDY > FOOOF > Fit power spectra. This calls pop_std_fooof(). The following menu will appear:

• Clusters, like component from the Tools UI, can be a colon vector, array, or single value.
• Fit mode is the dimension along which you would like to fit your spectra. "group" will fit every component within the design, while "across design" will only fit the averaged designs. For n designs, "group" will save a n × 1 struct in the FOOOF results for the desired cluster, while "across design" will save a a 1 × n struct.

As mentioned, Fit power spectra will add a struct to STUDY.etc called FOOOF_results, with the following contents (the shape of which depends on the design). For example:

>> STUDY.etc.FOOOF_results{i}

ans = 

1×3 struct array with fields:

    aperiodic_params
    peak_params
    gaussian_params
    error
    r_squared
    freqs
    power_spectrum
    fooofed_spectrum
    ap_fit

To plot a fit, select STUDY > FOOOF > Plot cluster fit. This calls pop_std_fooofplot(). This will plot the fit for each design variable (averaged over components). The following menu will appear:

• Cluster: This accepts a single cluster ID.
  
• Plot in loglog: is a boolean.

The plot uses fooof_mat's plotting functionality:

If you use this code in your project, please cite:

Donoghue T, Haller M, Peterson EJ, Varma P, Sebastian P, Gao R, Noto T, Lara AH, Wallis JD,
Knight RT, Shestyuk A, & Voytek B (2020). Parameterizing neural power spectra into periodic
and aperiodic components. Nature Neuroscience, 23, 1655-1665.
DOI: 10.1038/s41593-020-00744-x

• https://github.com/fooof-tools/fooof
• https://github.com/fooof-tools/fooof_mat
• https://github.com/sccn/eeglab/blob/develop/functions/popfunc
• https://github.com/sccn/eeglab/blob/develop/functions/studyfunc