XDF is a general-purpose container format for multi-channel time series data with extensive associated meta information. XDF is tailored towards biosignal data such as EEG, EMG, EOG, ECG, GSR, MEG, but it can also handle data with high sampling rate (like audio) or data with a high number of channels (like fMRI or raw video). Meta information is stored as XML.

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

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.

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

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.

YASA is a Python package to analyze polysomnographic sleep recordings.

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

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

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.

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

Python parser for Igor Binary Waves (.ibw) and Packed Experiment (.pxp) files written by WaveMetrics' IGOR Pro software.

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

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

This package provides a new backend based on pyqtgraph for the 2D-Data-Browser in MNE-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 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.

The table remodeler provides a flexible, operation-based framework for transforming tabular data files through JSON-configurable pipelines. Originally extracted from the hed-python remodeling tools, this package operates as a standalone tool while maintaining compatibility with HED annotations via the hedtools dependency.

Key features:

• Operation-based architecture for reproducible data transformations

• JSON-configurable pipelines for batch processing

• Support for HED-annotated event files (via hedtools package)

• Built-in backup and restore functionality

• Both programmatic API and command-line interface

• Extensible: create custom operations by extending BaseOp

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.

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 additional functionality for working with MNE-Python, the most popular Python package for processing electrophysiological data (EEG, MEG, ...).

Features:

• Reading additional file formats

• Inspecting files before reading

• Writing raw data

• ICLabel classification

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.

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