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

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.

This package provides a cross-platform interactive viewer to inspect the final results and quality of any spike sorter supported by spikeinterface.

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-Connectivity is an open-source Python package for connectivity and related measures of MEG, EEG, or iEEG data built on top of the MNE-Python API. It includes modules for data input/output, visualization, common connectivity analysis, and post-hoc statistics and processing.

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).

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.

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

Picard provides Python/Octave/MATLAB code for the preconditionned ICA for real 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 package provides utilities for reading the files produced by BIOPAC's AcqKnowledge software.

mne-denoise provides powerful signal denoising techniques for the MNE-Python ecosystem, including Denoising Source Separation (DSS) and ZapLine algorithms. These methods excel at extracting signals of interest by exploiting data structure rather than just variance.

mffpy is a lean reader for EGI's MFF file format. These files are directories containing several files of mostly xml files, but also binary files.

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

nwb2bids reorganizes NWB files into a BIDS directory layout.

Features:

• Automatically renames NWB files and their directories to conform to BIDS conventions.

• Extracts relevant metadata from NWB files to populate BIDS sidecar TSV & JSON files.

• Currently supports BEP32 (micro-electrode electrophysiology) data types, such as extracellular (ecephys) and intracellular (icephys) electrophysiology, as well as associated behavioral events.

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

Fast, efficient, and physiologically-informed tool to parameterize neural power spectra

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

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.

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

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.

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

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

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