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

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).

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

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.

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.

YASA is a command-line sleep analysis toolbox in Python. The main functions of YASA are:

• Automatic sleep staging of polysomnography data (see eLife article).

• Event detection: sleep spindles, slow-waves and rapid eye movements, on single or multi-channel EEG data.

• Artefact rejection, on single or multi-channel EEG data.

• Spectral analyses: bandpower, phase-amplitude coupling, 1/f slope, etc.

• Hypnogram analysis: sleep statistics, stage transitions, visualization, and manipulation.

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

This package provides a Python interface to the LSL. LSL is an overlay network for real-time exchange of time series between applications, most often used in research environments. LSL has clients for many other languages and platforms that are compatible with each other.

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

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

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

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.

MNE Qt Browser is an alternative backend for plotting multichannel time series data (such as EEG or MEG) with MNE-Python. The backend is based on PyQtGraph, which in turn uses Qt under the hood.

This package provides an implementation of RSA in Python.

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

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

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.

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

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.

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

Tensorpac is an open-source Python toolbox for computing PAC using tensors and parallel computing. This software provides a modular implementation which allows one to combine existing methods for measuring PAC and chance distribution.

This package provides a Python-native package for reading, writing, processing, and plotting physiologic signal and annotation data. The core I/O functionality is based on the WFDB specifications.

WFDB defines a set of open standards and software tools for the storage, sharing, and analysis of physiologic signal and annotation data. It has been widely used for decades to support biomedical research, clinical studies, and education.

This package provides an electrophysiological data analysis library for Python.