pyVHDLModel provides an unified abstract language model for VHDL written in Python.

GHDL analyses, elaborates and simulates VHDL sources. It may also be used as an experimental synthesizer backend.

GHDL Language Server Protocol (LSP) is a server for VHDL based on GHDL.

This package gathers GNAT binaries from FSF GCC releases of the Alire Project.

UVVM Light is a low threshold version of UVVM and is intended for developers who want to start using UVVM Utilty library and Bus Functional Models.

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

MNE-LSL (Documentation website) provides a real-time brain signal streaming framework. MNE-LSL contains an improved python-binding for the Lab Streaming Layer C++ library, mne_lsl.lsl, replacing pylsl. This low-level binding is used in high-level objects to interact with LSL streams.

MNE-CPP is an open-source, cross-platform C++ framework for real-time and offline processing of MEG, EEG, and related neurophysiological data.

phy is an open-source graphical interface for visualizing and manually curating large-scale electrophysiology datasets. It is optimized for high-density probes, including Neuropixels, and provides both an interactive curation workflow and Python extension points for specialized labs.

This is a library to automatically reject bad trials and repair bad sensors in M/EEG data.

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.

This is a Python package for performing representational similarity analysis (RSA) using MNE-Python data structures. The main use-case is to perform RSA using a “searchlight” approach through time and/or a volumetric or surface source space.

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.

pyRiemann is a Python package for machine learning with multivariate data, using the Riemannian geometry of symmetric (resp. Hermitian) positive definite (SPD) (resp. HPD) matrices. It provides a high-level interface fully compatible with scikit-learn, making it easy to build powerful pipelines for classification, regression, and clustering.

Key features:

• Riemannian Geometry – Leverage distances, means, and tangent space projections on the manifold of SPD/HPD matrices.

• scikit-learn Compatible – All estimators follow the scikit-learn API. Build pipelines, use cross-validation, and grid search out of the box.

• Brain-Computer Interfaces – State-of-the-art processing and classification of EEG, MEG, and fNIRS signals.

• Remote Sensing – Apply Riemannian clustering to radar image segmentation.

• Transfer Learning – Domain adaptation tools for cross-session and cross-subject generalization.

• NumPy and PyTorch Backends – Core geometry functions support both backends transparently through the Python Array API, enabling optional GPU acceleration and autograd when PyTorch tensors are passed in.

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

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

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

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

NeuroKit2 is a user-friendly package providing easy access to advanced biosignal processing routines. Researchers and clinicians without extensive knowledge of programming or biomedical signal processing can analyze physiological data with only two lines of code.

Pymef is a wrapper library for MEF developed by the MSEL laboratory.

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

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

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

This is a library to run the PICARD algorithm and its orthogonal version (PICARD-O). These algorithms show fast convergence even on real data for which sources independence do not perfectly hold.