This is an open-source tool which allows to load CURRY data into Python. It supports: raw float (.cdt), ascii (.cdt), legacy raw float (.dat) and legacy ascii (.dat).

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.

A Python package to handle the layout, geometry, and wiring of silicon probes for extracellular electrophysiology experiments.

This package provides HED validation, summary, and analysis tools for annotating events and experimental metadata.

FASTER is a fully automated, unsupervised method for processing of high density EEG data.

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.

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

edfio is a Python package for reading and writing EDF and EDF+C files.

This package provides a C++ library for multi-modal time-synched data transmission over the local network.

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.

BioSPPy is a toolbox for biosignal processing written in Python. The toolbox bundles together various signal processing and pattern recognition methods geared torwards the analysis of biosignals.

This library implements a simple lossless compression scheme adapted to time-dependent high-frequency, high-dimensional signals. It is being developed within the International Brain Laboratory with the aim of being the compression library used for all large-scale electrophysiological recordings based on Neuropixels. The signals are typically recorded at 30 kHz and 10 bit depth, and contain several hundreds of channels.

pyRiemann is a Python machine learning package based on scikit-learn API. It provides a high-level interface for processing and classification of real (resp. complex)-valued multivariate data through the Riemannian geometry of symmetric (resp. Hermitian) positive definite (SPD) (resp. HPD) matrices.

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

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

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.

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.

This package provides a Python library implementing the DICS beamformer for connectivity analysis and power mapping on the cortex.

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

This inspector is meant as a companion to the PyNWB validator, which checks for strict schema compliance. This tool attempts to apply some common sense to find components of the file that are technically compliant, but possibly incorrect, suboptimal in their representation, or deviate from best practices.

PyBVRF is a Python package for working with BVRF files.

The package includes the following features:

• Support for multi-participant recordings

• Seamless integration with MNE-Python

• Convenient access to metadata (including the original YAML header)

• Support for markers and impedance data

A BVRF recording consists of multiple files which are expected to be available in the same directory. The required files are:

• <fname>.bvrh (header file)

• <fname>.bvrd (data file)

• <fname>.bvrm (marker file)

Optionally, <fname>.bvri (impedance file) may also be present.

MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, ECoG, and more. It includes modules for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, statistics, and more.

This package provides an electrophysiological data analysis library for Python.

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