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

spike sorting pipeline.

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

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

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

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.

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.

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

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

This is a library to automatically reject bad trials and repair bad sensors in magneto-/electroencephalography (M/EEG) data.

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.

A Python package to handle the layout, geometry, and wiring of silicon probes for extracellular electrophysiology experiments.

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

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.

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.

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

edfio is a Python package for reading and writing EDF and EDF+C files.

This is a Python package for performing representational similarity analysis (RSA) using MNE-Python data structures. The main use-case is to perform RSA using a “searchlight” approach through time and/or a volumetric or surface source space.

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.