Metadata-Version: 2.0
Name: fooof
Version: 0.1.2
Summary: Fitting oscillations & one-over f
Home-page: https://github.com/voytekresearch/fooof
Author: The Voytek Lab
Author-email: voyteklab@gmail.com
License: Apache License, 2.0
Download-URL: https://github.com/voytekresearch/fooof/releases
Description-Content-Type: UNKNOWN
Keywords: neuroscience,neural oscillations,power spectra,1/f,electrophysiology
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Requires-Dist: numpy
Requires-Dist: scipy (>=0.19.0)
Provides-Extra: all
Requires-Dist: matplotlib; extra == 'all'
Requires-Dist: pytest; extra == 'all'
Provides-Extra: plot
Requires-Dist: matplotlib; extra == 'plot'
Provides-Extra: tests
Requires-Dist: pytest; extra == 'tests'


========================================
FOOOF: 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


