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 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).

Python library for reading, writing, and validating SNIRF 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.

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.

mne-icalabel is a Python package for labeling independent components that stem from an Independent Component Analysis (ICA).

spike sorting pipeline.

This package provides denoising tools for M/EEG processing in Python.

This package provides a full-flegded processing pipeline for your MEG and EEG data. It operates on data stored according to the BIDS format.

This package provides an electrophysiological data analysis library for Python.

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

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

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.

This package contains code to compute the standard EEG electrode locations on a spherical head model for the 10-20, 10-10, and 10-05 system.

This package provides a Python implementation of a multitaper window method for estimating Wigner spectra for certain locally stationary processes.

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

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.

This package provides a simple open source Python package for EEG microstate segmentation.

YASA is a Python package to analyze polysomnographic sleep recordings.

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

Tensor-based Phase-Amplitude Coupling.