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

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

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.

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

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

This package provides tools for finding sharp-wave ripple events (150-250 Hz) from local field potentials.

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 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.

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.

A Python-native package for reading, writing, processing, and plotting physiologic signal and annotation data. The core I/O functionality is based on the Waveform Database (WFDB) specifications.

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

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

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

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.

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

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).

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.

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

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

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.

pyABF is a Python package for reading electrophysiology data from ABF files. It was created with the goal of providing a Pythonic API to access the content of ABF files which is so intuitive to use (with a predictive IDE) that documentation is largely unnecessary.

This package provides a new backend based on pyqtgraph for the 2D-Data-Browser in MNE-Python.

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