This package provides tools for finding sharp-wave ripple events (150-250 Hz) from local field potentials.

PyNWB is a Python package for working with NWB files. It provides a high-level API for efficiently working with Neurodata stored in the NWB format.

AntroPy is a Python package providing several time-efficient algorithms for computing the complexity of time-series. It can be used for example to extract features from EEG signals.

Neo is a package for representing electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats.

SpikeInterface is a Python framework designed to unify preexisting spike sorting technologies into a single code base.

It can:

• read/write many extracellular file formats.

• pre-process extracellular recordings.

• run many popular, semi-automatic spike sorters (kilosort1-4, mountainsort4-5, spykingcircus, tridesclous, ironclust, herdingspikes, yass, waveclus)

• run sorters developed in house (lupin, spkykingcicus2, tridesclous2, simple) that compete with kilosort4

• run theses polar sorters without installation using containers (Docker/Singularity).

• post-process sorted datasets using th SortingAnalyzer

• compare and benchmark spike sorting outputs.

• compute quality metrics to validate and curate spike sorting outputs.

• visualize recordings and spike sorting outputs in several ways (matplotlib, sortingview, jupyter, ephyviewer)

• export a report and/or export to phy

• curate your sorting with several strategies (ml-based, metrics based, manual, ...)

• have powerful sorting components to build your own sorter.

• have a full motion/drift correction framework.

track_linearization is a Python package for mapping animal movement on complex track environments (mazes, figure-8s, T-mazes) into simplified 1D representations. It uses Hidden Markov Models to handle noisy position data and provides powerful tools for analyzing spatial behavior in neuroscience experiments.

pybv is a lightweight I/O utility for the BrainVision data format. The BrainVision data format is a recommended data format for use in the Brain Imaging Data Structure.

pyEDFlib is a Python library to read/write EDF+/BDF+ files based on EDFlib. EDF means European Data Format

The OpenMEEG software is a C++ package for solving the forward problems of electroencephalography (EEG) and magnetoencephalography (MEG).

Meggie is an open-source software designed for intuitive MEG and EEG analysis. With its user-friendly graphical interface, Meggie brings the powerful analysis methods of MNE-Python to researchers without requiring programming skills.

MNELAB is a GUI for MNE-Python, a Python package for EEG/MEG analysis.

Python library for reading, writing, and validating SNIRF files

MNE-Connectivity is an open-source Python package for connectivity and related measures of MEG, EEG, or iEEG data built on top of the MNE-Python API. It includes modules for data input/output, visualization, common connectivity analysis, and post-hoc statistics and processing.

This package provides I/O functions for the CNT format from ANT Neuro.

The NIX data model allows to store fully annotated scientific dataset, i.e. the data together with its metadata within the same container. The current implementations store the actual data using the HDF5 file format as a storage backend.

This package provides a cross-platform interactive viewer to inspect the final results and quality of any spike sorter supported by spikeinterface.

Picard provides Python/Octave/MATLAB code for the preconditionned ICA for real data.

This package provides a library for sleep stage classification using ECG data.

This is a library to perform shift-invariant sparse dictionary learning, also known as convolutional sparse coding (CSC), on time-series data.

mffpy is a lean reader for EGI's MFF file format. These files are directories containing several files of mostly xml files, but also binary files.

A simple python package for fitting L2- and smoothing-penalized generalized linear models. Built primarily because the statsmodels GLM fit_regularized method is built to do elastic net (combination of L1 and L2 penalities), but if you just want to do an L2 or a smoothing penalty (like in generalized additive models), using a penalized iteratively reweighted least squares (p-IRLS) is much faster.

spike sorting pipeline.

This package implements both parametric and permutation-based ARI, and is meant to be compatible with the MNE-Python ecosystem.

bycycle is a tool for quantifying features of neural oscillations in the time domain, as opposed to the frequency domain, using a cycle-by-cycle approach.