This package provides a Python library implementing the DICS beamformer for connectivity analysis and power mapping on the cortex.

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.

Picard provides Python/Octave/MATLAB code for the preconditionned ICA for real data.

MNE-LSL (Documentation website) provides a real-time brain signal streaming framework. MNE-LSL contains an improved python-binding for the Lab Streaming Layer C++ library, mne_lsl.lsl, replacing pylsl. This low-level binding is used in high-level objects to interact with LSL streams.

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

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.

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.

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

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.

This package provides an electrophysiological data analysis library for Python.

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

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.

YASA is a Python package to analyze polysomnographic sleep recordings.

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

HED is a framework for systematically describing both laboratory and real-world events as well as other experimental metadata. HED tags are comma-separated path strings that provide a standardized vocabulary for annotating events and experimental conditions.

Key Features:

• Validate HED annotations against schema specifications

• Analyze and summarize HED-tagged datasets

• Full HED support in BIDS (Brain Imaging Data Structure)

• HED support in NWB (Neurodata Without Borders) when used the ndx-hed extension.

• Platform-independent and data-neutral

• Command-line tools and Python API

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.

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.

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

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.

This package provides utilities for reading the files produced by BIOPAC's AcqKnowledge software.

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.

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

This package provides a simple open source Python package for EEG microstate segmentation.

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