mne
MNE-Python project for MEG and EEG data analysis.
Decision gist · record as of 2026-08-14
Yes. MNE-Python is a well-established, actively maintained toolkit with no known vulnerabilities, permissive BSD-3-Clause licensing, and low install friction. It is the standard choice for neurophysiological data analysis in Python. Install it if you work with MEG, EEG, or related brain recordings.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python ≥3.10.
- Typical use requires neurophysiological data files (MEG, EEG, etc.) in supported formats.
- Low friction installation via pip with a stable, actively maintained codebase.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause permissive license allows use in commercial and private projects with minimal restrictions, requiring only preservation of copyright and license notices.
last release 2026-04-20 (116 days) · last repo commit 2026-08-12 · 3,482 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 4,018,846 downloads/mo, #2,397 on PyPI
Alternatives
Verify before relying
pip install mne
import mne
raw = mne.io.read_raw_fif('sample_data.fif')
raw.plot()- Whether optional dependencies for advanced visualization or specific file formats are available and their install friction.
- Performance characteristics for large datasets or real-time processing scenarios.
- Specific machine learning capabilities and their scope relative to dedicated ML libraries.
What it is and what it does
MNE-Python is a mature, open-source toolkit for working with human brain recordings. It handles data from multiple modalities—MEG, EEG, sEEG, ECoG, fNIRS—and provides a complete pipeline from raw data import and preprocessing to visualization and statistical analysis. The package bundles modules for source localization, spectral and time-frequency decomposition, functional connectivity, and machine learning on neural data.
The package is built on standard scientific Python dependencies including numpy, scipy, and matplotlib, and includes convenience functions for common neuroimaging workflows. It is widely used in neuroscience research and clinical settings. Installation is straightforward via pip, and the codebase is actively maintained with comprehensive documentation and a user forum for support.
Use it for
- Load and preprocess raw MEG or EEG recordings, apply filters, and detect artifacts before analysis.
- Estimate the location of neural sources in the brain from surface recordings using inverse methods.
- Compute spectrograms, power spectral density, or cross-frequency coupling in neurophysiological data.
- Analyze functional connectivity between brain regions using correlation or coherence measures.
- Prepare neural data for machine learning pipelines for classification or regression on brain signals.
- Visualize brain activity on cortical surfaces or as time-series plots for exploratory analysis.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
MNE-Python is a well-established, actively maintained toolkit with no known vulnerabilities, permissive BSD-3-Clause licensing, and low install friction. It is the standard choice for neurophysiological data analysis in Python. Install it if you work with MEG, EEG, or related brain recordings.
Install
mne on PyPI
Before you install
Low friction installation via pip with a stable, actively maintained codebase. Last release 116 days ago with active repository (3482 stars, last commit 2026-08-12). Requires Python ≥3.10 and nine well-established scientific dependencies.
Requires Python ≥3.10. Typical use requires neurophysiological data files (MEG, EEG, etc.) in supported formats.
License in practice
BSD-3-Clause permissive license allows use in commercial and private projects with minimal restrictions, requiring only preservation of copyright and license notices.
Quickstart
pip install mne
import mne
raw = mne.io.read_raw_fif('sample_data.fif')
raw.plot()Verify before relying
- Whether optional dependencies for advanced visualization or specific file formats are available and their install friction.
- Performance characteristics for large datasets or real-time processing scenarios.
- Specific machine learning capabilities and their scope relative to dedicated ML libraries.
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagesdecoratorjinja2lazy-loadermatplotlibnumpypackagingpoochscipytqdm |
| Maintenance | Actively maintained 116 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 4,018,846 / month, #2,397 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI ApprovedOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3Topic :: Scientific/EngineeringTopic :: Software Development |
Evidence: mne-1.12.1-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “MEG EEG analysis”
- mneMNE-Python provides tools for exploring, visualizing, and analyzing…
- mne-bidsMNE-BIDS reads and writes neuroimaging datasets in BIDS format,…
- pyriemannpyRiemann provides scikit-learn-compatible machine learning…
Give your agent the search over MCP, or paste the wish link into any chat.
More Software Development packages
Provides backported and experimental type hints for Python 3.9+, allowing use of newer typing features on older Python versions and enabling early experimentation with type system PEPs before they enter the standard library.
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides a way to document function parameters, class attributes, return types, and variables inline using Python's `Annotated` type hint syntax instead of traditional docstrings.
Typer builds command-line applications from Python functions using type hints, automatically generating help text, argument parsing, and shell completion.
Install it if you are building CLIs in Python.
Distlib provides low-level packaging utilities for building, distributing, and managing Python software—including metadata handling, version specifiers, wheel support, script installation, and dependency resolution.
See also mne-bids · neo · nilearn · nipype · antropy · neurokit2 · pyriemann · pynwb · dipy · edfio