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.

spectral_connectivity is a Python software package that computes multitaper spectral estimates and frequency-domain brain connectivity measures such as coherence, spectral granger causality, and the phase lag index using the multitaper Fourier transform.

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

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

This package provides a Python library for reading, writing, and validating SNIRF files.

pybv is a lightweight I/O utility for the BrainVision data format.

The BrainVision data format is a recommended data format for use in the Brain Imaging Data Structure.

BrainVision is the name of a file format commonly used for storing electrophysiology data. Originally, it was put forward by the company Brain Products, however the simplicity of the format has allowed for a diversity of tools reading from and writing to the format.

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.

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.

probeinterface is a Python package to handle probe layout, geometry and wiring to a device for neuroscience experiments.

The package handles the following items:

• probe geometry (2D or 3D layout)

• probe shape (contours of the probe)

• shape and size of the shank

• probe wiring to the recording device

• combination of several probes: global geometry + global wiring

The probeinterface package also provides:

• basic plotting functions with matplotlib

• input/output functions to several formats (PRB, NWB, CSV, MEArec, SpikeGLX, …)

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.

This package provides group sequential tests for EEG and fMRI data.

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.

Kilosort4 is a Python package for fast spike sorting with template matching. Spike sorting is the computational process of extracting the firing times of single neurons from recordings of local electrical fields.

This package provides a module for KIT MEG coregistration for MNE-Python.

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.

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.

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

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

PyPREP: is a Python implementation of the Preprocessing Pipeline (PREP) for EEG data, working with MNE-Python.

MNE-BIDS is a Python package for organizing electrophysiological data into the BIDS format and facilitating their analysis with MNE-Python.

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

pynapple is a light-weight python library for neurophysiological data analysis. The goal is to offer a versatile set of tools to study typical data in the field, i.e. time series (spike times, behavioral events, etc.) and time intervals (trials, brain states, etc.). It also provides users with generic functions for neuroscience such as tuning curves and cross-correlograms.

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.