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Digital signal processing for neural time series.

Project description

ProjectStatus Version BuildStatus Coverage License PythonVersions Publication

Tools to analyze and simulate neural time series, using digital signal processing.

Overview

neurodsp is a collection of approaches for applying digital signal processing, and related algorithms, to neural time series. It also includes simulation tools for generating plausible simulations of neural time series.

Available modules in NeuroDSP include:

  • filt : Filter data with bandpass, highpass, lowpass, or notch filters

  • timefrequency : Estimate instantaneous measures of oscillatory activity

  • spectral : Compute freqeuncy domain features such as power spectra

  • burst : Detect bursting oscillations in neural signals

  • rhythm : Find and analyze rhythmic and recurrent patterns in time series

  • aperiodic : Analyze aperiodic features of neural time series

  • sim : Simulate time series, including periodic and aperiodic signal components

  • plts : Plot neural time series and derived measures

  • utils : Additional utilities for managing time series data

Documentation

Documentation for the NeuroDSP module is available here.

The documentation includes:

  • Tutorials: which describe and work through each module in NeuroDSP

  • Examples: demonstrating example applications and workflows

  • API List: which lists and describes all the code and functionality available in the module

  • Glossary: which defines all the key terms used in the module

If you have a question about using NeuroDSP that doesn’t seem to be covered by the documentation, feel free to open an issue and ask!

Dependencies

NeuroDSP is written in Python, and requires Python >= 3.6 to run.

It has the following dependencies:

Optional dependencies:

  • pytest is needed if you want to run the test suite locally

We recommend using the Anaconda distribution to manage these requirements.

Install

The current major release of NeuroDSP is the 2.X.X series.

See the changelog for notes on major version releases.

Stable Release Version

To install the latest stable release, you can use pip:

$ pip install neurodsp

NeuroDSP can also be installed with conda, from the conda-forge channel:

$ conda install -c conda-forge neurodsp

Development Version

To get the current development version, first clone this repository:

$ git clone https://github.com/neurodsp-tools/neurodsp

To install this cloned copy, move into the directory you just cloned, and run:

$ pip install .

Editable Version

To install an editable version, download the development version as above, and run:

$ pip install -e .

Contribute

This project welcomes and encourages contributions from the community!

To file bug reports and/or ask questions about this project, please use the Github issue tracker.

To see and get involved in discussions about the module, check out:

  • the issues board for topics relating to code updates, bugs, and fixes

  • the development page for discussion of potential major updates to the module

When interacting with this project, please use the contribution guidelines and follow the code of conduct.

Reference

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

Cole, S., Donoghue, T., Gao, R., & Voytek, B. (2019). NeuroDSP: A package for
neural digital signal processing. Journal of Open Source Software, 4(36), 1272.
DOI: 10.21105/joss.01272

Direct Link: https://doi.org/10.21105/joss.01272

Bibtex:

@article{cole_neurodsp:_2019,
    title = {NeuroDSP: A package for neural digital signal processing},
    author = {Cole, Scott and Donoghue, Thomas and Gao, Richard and Voytek, Bradley},
    journal = {Journal of Open Source Software},
    year = {2019},
    volume = {4},
    number = {36},
    issn = {2475-9066},
    url = {https://joss.theoj.org/papers/10.21105/joss.01272},
    doi = {10.21105/joss.01272},
}

Funding

Supported by NIH award R01 GM134363 from the NIGMS.

https://www.nih.gov/sites/all/themes/nih/images/nih-logo-color.png

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