There is also a growing ecosystem of other Python packages that work alongside
MNE-Python, including:
alphaCSC: Convolutional dictionary learning for noisy signals.
antio: Python package to handle I/O with the CNT format from ANT Neuro.
autoreject: Automated rejection and repair of epochs in M/EEG.
best-python: The goal of this project is to provide a way to use the best-brainstorm Matlab solvers in Python, compatible with MNE-Python.
bycycle: Cycle-by-cycle analyses of neural oscillations.
conpy: Functions and classes for performing connectivity analysis on MEG data.
dcm2niix: DICOM to NIfTI converter
dipy: Diffusion MRI Imaging in Python
eeglabio: I/O support for EEGLAB files in Python
eelbrain: Open-source Python toolkit for MEG and EEG data analysis.
emd: Empirical Mode Decomposition in Python.
fooof: fitting oscillations & one-over f
fsleyes: FSLeyes is the FSL image viewer.
meggie: User-friendly graphical user interface to do M/EEG analysis
mffpy: Reader and Writer for Philips’ MFF file format.
mne-ari: All-Resolutions Inference for M/EEG
mne-bids: MNE-BIDS: Organizing MEG, EEG, and iEEG data according to the BIDS specification and facilitating their analysis with MNE-Python
mne-bids-pipeline: A full-flegded processing pipeline for your MEG and EEG data
mne-connectivity: mne-connectivity: A module for connectivity data analysis with MNE.
mne-faster: Code for performing the FASTER pipeline on MNE-Python data structures.
mne-features: MNE-Features software for extracting features from multivariate time series
mne-gui-addons: MNE-Python GUI addons.
mne-hcp: We provide Python tools for seamless integration of MEG data from the Human Connectome Project into the Python ecosystem
mne-icalabel: MNE-ICALabel: Automatic labeling of ICA components from MEG, EEG and iEEG data with MNE.
mne-kit-gui: A module for KIT MEG coregistration.
mne-lsl: Real-time framework integrated with MNE-Python for online neuroscience research through LSL-compatible devices.
mne-microstates: Code for microstate analysis, in combination with MNE-Python.
mne-nirs: An MNE compatible package for processing near-infrared spectroscopy data
mne-qt-browser: A new backend based on pyqtgraph for the 2D-Data-Browser in MNE-Python
mne-rsa: Code for performing Representational Similarity Analysis on MNE-Python data structures.
mnelab: A graphical user interface for MNE
neurodsp: Digital signal processing for neural time series.
neurokit2: The Python Toolbox for Neurophysiological Signal Processing.
niseq: Group sequential tests for neuroimaging
nitime: Nitime: timeseries analysis for neuroscience data
openmeeg: Forward problems solver in the field of EEG and MEG.
openneuro-py: A Python client for OpenNeuro.
pactools: Estimation of phase-amplitude coupling (PAC) in neural time series, including with driven auto-regressive (DAR) models.
posthoc: post-hoc modification of linear models
pybv: pybv – A lightweight I/O utility for the BrainVision data format.
pycrostates: A simple open source Python package for EEG microstate segmentation.
pyprep: A Python implementation of the preprocessing pipeline (PREP) for EEG data.
pyriemann: Machine learning for multivariate data with Riemannian geometry
neo: Neo is a package for representing electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats
python-picard: Preconditoned ICA for Real Data
sesameeg: Sequential Monte Carlo algorithm for multi dipolar source modeling in MEEG.
sleepecg: A package for sleep stage classification using ECG data
snirf: Interface and validator for SNIRF files
tensorpac: Tensor-based Phase-Amplitude Coupling
yasa: YASA: Analysis of polysomnography recordings.