# mne-tools/mne-python

MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python

Repository: https://github.com/mne-tools/mne-python
Canonical: https://ross.abutalabs.com/products/mne-python
Homepage: https://mne.tools
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: python, neuroscience, electroencephalography, magnetoencephalography, electrocorticography, machine-learning, statistics, visualization, eeg, meg, ecog, neuroimaging
Last push: 2026-08-26T17:40:27+00:00

## Health v2 (maintenance only)
Score: 88/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 68, longevity 100
- inputs: {"age_days": 5696, "days_push": 7, "days_rel": 135, "gap_med": 50.5, "n_releases_24m": 7}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3495, forks 1585 (observed 2026-08-28T04:08:07.325503+00:00)

## What it is
MNE-Python is an open-source Python library for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, and ECoG. It provides modules for data I/O, preprocessing, source estimation, time-frequency and connectivity analysis, statistics, and machine learning.

## Use cases
- analyze EEG data in Python
- process MEG recordings
- visualize neurophysiological signals
- perform source estimation from EEG/MEG
- run time-frequency analysis on brain signals
- compute connectivity analysis on MEG data
- apply machine learning to EEG data
- do statistics on neuroimaging data

## When to choose
- you need to read, preprocess, or analyze MEG/EEG/sEEG/ECoG data in Python
- you want source estimation or time-frequency analysis of brain signals
- you need publication-quality visualizations of neurophysiological data
- you want a comprehensive, well-documented neuroimaging library with an active community

## When to avoid
- you only need general-purpose signal processing without neuroimaging-specific features
- you work exclusively with fMRI or other non-MEG/EEG modalities better served by tools like Nilearn
- you need a GUI-only workflow without programming

## Facets
- artifact type: library
- maturity: stable
- function: data-visualization, machine-learning, nlp, data-science, image-processing
- domain: data-science, data-visualization, machine-learning
- platform: python, cross-platform
- tags: eeg, meg, neuroimaging, ecog, seeg, neurophysiology, time-frequency-analysis, source-estimation, signal-processing, brain-imaging, neuroscience

## Member repositories
- mne-tools/mne-python (main) score 88

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:07.325503+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:35:54.598116+00:00, confidence not recorded.
  - readme: https://github.com/mne-tools/mne-python (fetched 2026-08-28T04:08:07.325503+00:00, sha 5696aa37ef37)
  - homepage: https://mne.tools (fetched 2026-08-29T09:30:02.635332+00:00, sha 60c3ee0c41bf)
- Data as of 2026-08-30T08:39:29.467469+00:00.