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Find Neuropyhsiological Networks (FiNN)

A Python Toolbox for the analysis of electrophysiological data

The main directory finn contains the toolbox itself, while finn_demo contains a number of demo files describing end-point functionality of the toolbox. finn_tests contains automated tests to perform unit tests in order to evaluate the state of the toolbox.

Installation

FiNN can be installed either

  1. manually by downloading a branch of choice
  2. via pip when calling pip install finnpy

For more information please click here.

Branches

FiNN offers two branches:

  • Stable: Contains only stable releases (passed all unit tests)
  • Develop: Contains all latest changes and may be unstable (not unit tested)

Features

Currently implemented features in this toolbox. Full documentation is accessible in doc/_build/index.html. Documentation of the development branch is available here.

  • Artifact rejection
    • Identification of bad channels and (optional) restoration of those from neighboring channels
    • Removal of statistical outliers (identified via z-transform)
  • Basic
    • Functionality to downsample data
    • Common average re-referencing
  • Connectivity
    • Cross frequency coupling
      • Implemented methods are phase lock value, modulation index, mean vector length, and direct modulation index.
    • Same frequency coupling
      • Implemented methods are weighted phase lag index (wPLI), phase slope index, magnitude squared coherence, imaginary coherence, and directional absolute coherence.
  • File IO
    • Load data from brain vision recordings.
    • A data manager to save & load (unbalanced) data with a minimal memory footprint.
  • Frequency spectrum filters
    • Easy access to scipys butterworth filter
    • An overlap add based implementation of an FIR filter
  • Misc.
    • A parallelization loop for easy access to parallel processing. Accustomed to minimal memory footprint and resource consumption
  • Statistics
    • Easy within Python access to generalized linear mixed models
  • Visualization
    • Plot topographical maps of EEG recordings. Effect sizes may be visualized by color whereas significance may be visualized using dots (black - n.s., half-filled - significant before multiple comparison correction, white - significant after multiple comparison correction)

Requirements

  • Python 3.6 or above.
  • R and R packages
    • lm4
    • car
    • carData
    • Matrix

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A Python Toolbox for the analysis of electrophysiological data

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