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.

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

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

Elephant (Electrophysiology Analysis Toolkit) is an open-source, community centered library for the analysis of electrophysiological data in the Python programming language. The focus of Elephant is on generic analysis functions for spike train data and time series recordings from electrodes, such as the local field potentials (LFP) or intracellular voltages. In addition to providing a common platform for analysis code from different laboratories, the Elephant project aims to provide a consistent and homogeneous analysis framework that is built on a modular foundation. Elephant is the direct successor to Neurotools and maintains ties to complementary projects such as OpenElectrophy and spykeviewer.

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.

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

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 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).

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.

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

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

This package provides an electrophysiological data analysis library for Python.

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

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

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

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

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).

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

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 an implementation of RSA in Python.

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

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.

Python parser for Igor Binary Waves (.ibw) and Packed Experiment (.pxp) files written by WaveMetrics' IGOR Pro software.