This package provides an electrophysiological data analysis library for Python.

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

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.

Fast, efficient, and physiologically-informed tool to parameterize neural power spectra

klusta is an open source package for automatic spike sorting of multielectrode neurophysiological recordings made with probes containing up to a few dozens of sites.

MNE-BIDS is a Python package that allows you to read and write BIDS-compatible datasets with the help of MNE-Python.

pyABF is a Python package for reading electrophysiology data from ABF files. It was created with the goal of providing a Pythonic API to access the content of ABF files which is so intuitive to use (with a predictive IDE) that documentation is largely unnecessary.

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.

Elephant (Electrophysiology Analysis Toolkit) is an open-source, community centered library for the analysis of electrophysiological data in the Python programming language. The focus of Elephant is on generic analysis functions for spike train data and time series recordings from electrodes, such as the local field potentials (LFP) or intracellular voltages. In addition to providing a common platform for analysis code from different laboratories, the Elephant project aims to provide a consistent and homogeneous analysis framework that is built on a modular foundation. Elephant is the direct successor to Neurotools and maintains ties to complementary projects such as OpenElectrophy and spykeviewer.

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 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.

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

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

nwb2bids reorganizes NWB files into a BIDS directory layout.

Features:

• Automatically renames NWB files and their directories to conform to BIDS conventions.

• Extracts relevant metadata from NWB files to populate BIDS sidecar TSV & JSON files.

• Currently supports BEP32 (micro-electrode electrophysiology) data types, such as extracellular (ecephys) and intracellular (icephys) electrophysiology, as well as associated behavioral events.

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

This package provides tools for calculating smoothed 2D position, speed, head direction.

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.

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.

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

Python library for reading, writing, and validating SNIRF files

mne-denoise provides powerful signal denoising techniques for the MNE-Python ecosystem, including Denoising Source Separation (DSS) and ZapLine algorithms. These methods excel at extracting signals of interest by exploiting data structure rather than just variance.

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.

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