{ "info": { "author": "The Voytek Lab", "author_email": "voyteklab@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Operating System :: MacOS :: MacOS X", "Operating System :: Microsoft :: Windows", "Operating System :: POSIX :: Linux", "Operating System :: Unix", "Programming Language :: Python", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Topic :: Scientific/Engineering" ], "description": "\n========================================\nFOOOF: Fitting Oscillations & One-Over F\n========================================\n\nFOOOF is a fast, efficient, physiologically-informed model to parameterize neural power spectra,\ncharacterizing both the aperiodic 'background' component, and periodic components as overlying peaks,\nreflecting putative oscillations.\n\nThe model conceives of the neural power spectrum as consisting of two distinct functional processes:\n1) an aperiodic component, typically reflecting 1/f like characteristics, modeled with an exponential fit, with:\n2) band-limited peaks rising above this background, reflecting putative oscillations, and modeled as Gaussians.\n\nWith regards to examing peaks in the frequency domain, as putative oscillations, the benefit\nof the FOOOF approach is that these peaks are characterized in terms of their specific center\nfrequency, amplitude and bandwidth without requiring predefining specific bands of interest.\nIn particular, it separates these peaks from a dynamic, and independently interesting 1/f\nbackground. 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