fooof 0.1.3

Fitting oscillations & one-over f

FOOOF is a fast, efficient, physiologically-informed model to parameterize neural power spectra, characterizing both the aperiodic ‘background’ component, and periodic components as overlying peaks, reflecting putative oscillations.

The model conceives of the neural power spectrum as consisting of two distinct functional processes: 1) an aperiodic component, typically reflecting 1/f like characteristics, modeled with an exponential fit, with: 2) band-limited peaks rising above this background, reflecting putative oscillations, and modeled as Gaussians.

With regards to examing peaks in the frequency domain, as putative oscillations, the benefit of the FOOOF approach is that these peaks are characterized in terms of their specific center frequency, amplitude and bandwidth without requiring predefining specific bands of interest. In particular, it separates these peaks from a dynamic, and independently interesting 1/f background. This conception of the 1/f as potentially functional (and therefore worth carefully modeling) is based on work from the Voytek lab and others that collectively shows that 1/f changes across task demands and participant demographics, and that it may index underlying excitation/inhibition (EI) balance.

A full description of the method and approach is available in the paper linked below.

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

Haller M, Donoghue T, Peterson E, Varma P, Sebastian P, Gao R, Noto T, Knight RT, Shestyuk A, Voytek B (2018) Parameterizing Neural Power Spectra. bioRxiv, 299859. doi: https://doi.org/10.1101/299859

Paper Link: https://www.biorxiv.org/content/early/2018/04/11/299859