HED is a framework for systematically describing both laboratory and real-world events as well as other experimental metadata. HED tags are comma-separated path strings that provide a standardized vocabulary for annotating events and experimental conditions.

Key Features:

• Validate HED annotations against schema specifications

• Analyze and summarize HED-tagged datasets

• Full HED support in BIDS (Brain Imaging Data Structure)

• HED support in NWB (Neurodata Without Borders) when used the ndx-hed extension.

• Platform-independent and data-neutral

• Command-line tools and Python API

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

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 an implementation of RSA in Python.

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

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

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.

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.

SleepECG provides tools for sleep stage classification when EEG signals are not available. Based only on ECG, SleepECG provides functions for

• downloading and reading open polysomnography datasets,

• detecting heartbeats from ECG signals, and

• classifying sleep stages (which includes preprocessing, feature extraction, and classification).

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.

SESAMEEG is a Python3 library providing the Bayesian multi-dipole localization method SESAME for the automatic estimation of brain source currents from MEEG data, either in the time domain and in the frequency domain.

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

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

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

This package provides code for feature extraction with M/EEG data.

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

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.

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

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 Python implementation of a multitaper window method for estimating Wigner spectra for certain locally stationary processes.

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

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

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

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.