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

SESAMEEG is a Python3 library providing the Bayesian multi-dipole localization method SESAME for the automatic estimation of brain source currents from MEEG data, either in the time domain and in the frequency domain.

Tensor-based Phase-Amplitude Coupling.

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.

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

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

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-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.

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

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

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.

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.

YASA is a Python package to analyze polysomnographic sleep recordings.

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.

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 provides code for feature extraction with M/EEG data.

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

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

This package provides an electrophysiological data analysis library for Python.

PyNWB is a Python package for working with NWB files. It provides a high-level API for efficiently working with Neurodata stored in the NWB format.

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

AntroPy is a Python package providing several time-efficient algorithms for computing the complexity of time-series. It can be used for example to extract features from EEG signals.

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

MNE-CPP is an open-source, cross-platform C++ framework for real-time and offline processing of MEG, EEG, and related neurophysiological data.