Neuro Digital Signal Processing Toolbox

Project Status: Active – The project has reached a stable, usable state and is being actively developed. Latest Version Build Status codecov License Supported Python Versions Binder

A package of tools to process, analyze, and simulate neural recordings as individual voltage time series, with specific focus on time and frequency domain analyses.


Documentation for the NeuroDSP module is available here.


NeuroDSP is written in Python, and requires Python >= 3.5 to run.

It has the following dependencies:

  • numpy
  • scipy
  • matplotlib
  • scikit-learn
  • pandas
  • pytest (optional)

We recommend using the Anaconda distribution to manage these requirements.


To install the latest release of neurodsp, you can install from pip:

$ pip install neurodsp

To get the development version (updates that are not yet published to pip), you can clone this repo.

$ git clone

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

$ pip install .


NeuroDSP includes the following modules, each of which have dedicated tutorials.

  • filt : Filter data with bandpass, highpass, lowpass, or notch filters
  • burst : Detect bursting oscillations in neural signals
  • laggedcoherence : Estimate rhythmicity using the lagged coherence measure
  • spectral : Compute spectral domain features such as power spectra
  • swm : Identify recurrent patterns in signals using sliding window matching
  • timefrequency : Estimate instantaneous measures of oscillatory activity
  • sim : Simulate periodic and aperiodic signal components
  • plts : Plotting functions