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.

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 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 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 the tools for computing phase-amplitude coupling, time delay estimation, and wave shape features using the bispectrum and bicoherence. Additional tools for computing amplitude-amplitude coupling, phase-phase coupling, and spatio-spectral filters are also provided.

mne-denoise brings together complementary spatial, spectral, adaptive, and source-informed denoising methods, with NumPy support and integration with MNE-Python objects.

MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, ECoG, and more. It includes modules for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, statistics, and more.

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

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.

The NIX data model allows to store fully annotated scientific dataset, i.e. the data together with its metadata within the same container. The current implementations store the actual data using the HDF5 file format as a storage backend.

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.

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

pynapple is a light-weight python library for neurophysiological data analysis. The goal is to offer a versatile set of tools to study typical data in the field, i.e. time series (spike times, behavioral events, etc.) and time intervals (trials, brain states, etc.). It also provides users with generic functions for neuroscience such as tuning curves and cross-correlograms.

This package provides support for reading and writing EEGLAB files in Python.

replay_trajectory_classification is a Python package for decoding spatial position represented by neural activity and categorizing the type of trajectory.

It has several advantages over decoders typically used to characterize hippocampal data:

• It allows for moment-by-moment estimation of position using small temporal time bins which allow for rapid movement of neural position and makes fewer assumptions about what downstream cells can integrate.

• The decoded trajectories can change direction and are not restricted to constant velocity trajectories.

• The decoder can use spikes from spike-sorted cells or use clusterless spikes and their associated waveform features to decode.

• The decoder can categorize the type of neural trajectory and give an estimate of the confidence of the model in the type of trajectory.

• Proper handling of complex 1D linearized environments.

• Ability to extract and decode 2D environments.

• Easily installable, documented code with tutorials on how to use the code.

• Fast computation using GPUs.

probeinterface is a Python package to handle probe layout, geometry and wiring to a device for neuroscience experiments.

The package handles the following items:

• probe geometry (2D or 3D layout)

• probe shape (contours of the probe)

• shape and size of the shank

• probe wiring to the recording device

• combination of several probes: global geometry + global wiring

The probeinterface package also provides:

• basic plotting functions with matplotlib

• input/output functions to several formats (PRB, NWB, CSV, MEArec, SpikeGLX, …)

mne-icalabel is a Python package for labeling independent components that stem from an ICA.

This package provides a module for KIT MEG coregistration for MNE-Python.

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.

FASTER is a fully automated, unsupervised method for processing of high density EEG data.

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 denoising tools for M/EEG processing in 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.