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.

MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, ECoG, and more. It includes modules for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, statistics, and more.

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

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

This package provides the tools for computing phase-amplitude coupling, time delay estimation, and wave shape features using the bispectrum and bicoherence. Additional tools for computing amplitude-amplitude coupling, phase-phase coupling, and spatio-spectral filters are also provided.

MNELAB is a GUI for MNE-Python, a Python package for EEG/MEG analysis.

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

This package provides HED validation, summary, and analysis tools for annotating events and experimental metadata.

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.

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

replay_trajectory_classification is a Python package for decoding spatial position represented by neural activity and categorizing the type of trajectory.

It has several advantages over decoders typically used to characterize hippocampal data:

• It allows for moment-by-moment estimation of position using small temporal time bins which allow for rapid movement of neural position and makes fewer assumptions about what downstream cells can integrate.

• The decoded trajectories can change direction and are not restricted to constant velocity trajectories.

• The decoder can use spikes from spike-sorted cells or use clusterless spikes and their associated waveform features to decode.

• The decoder can categorize the type of neural trajectory and give an estimate of the confidence of the model in the type of trajectory.

• Proper handling of complex 1D linearized environments.

• Ability to extract and decode 2D environments.

• Easily installable, documented code with tutorials on how to use the code.

• Fast computation using GPUs.

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

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.

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

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.

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

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

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.

Tensor-based Phase-Amplitude Coupling.

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

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

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.

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

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.