mne

library1.12.1pypypi✓ verified 22d ago

MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, ECoG, NIRS, and more. It provides modules for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, and statistics. The library is actively developed, with stable releases occurring frequently, typically every few months.

data ai-ml

pip install --upgrade mne

github ↗package ↗homepage ↗docs ↗

INSTALL

IMPORT

SIG · MNE

mne

datapythonv1.12.1

Install

13.6s avg

Import

480ms

Disk

352MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.12.1 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 0.484s · 348.1MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 13.6s · import 0.476s · 335MB

352MB installed

● package 352MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

mne

✓ import mne

Raw

✓ from mne.io import Raw

Epochs

✓ from mne import Epochs

Evoked

✓ from mne import Evoked

read_raw_fif

✓ from mne.io import read_raw_fif

✗ import mne.read_raw_fif

`read_raw_fif` is in the `mne.io` submodule, not directly under `mne`.

pick_types

✓ from mne.channels import pick_types

✗ import mne.pick_types

`pick_types` is in the `mne.channels` submodule.

This quickstart demonstrates loading a sample MEG/EEG dataset, applying a basic filter to the continuous data, defining events, creating epoched data, computing an evoked response, and finally plotting the evoked activity.

import mne import numpy as np # Set MNE_DATA for sample dataset (optional, will download if not found) os.environ['MNE_DATA'] = os.environ.get('MNE_DATA', mne.utils.get_config('MNE_DATA', default='')) # Load a sample dataset data_path = mne.datasets.sample.data_path() raw_fname = data_path / 'MEG' / 'sample' / 'sample_audvis_raw.fif' # Read raw data raw = mne.io.read_raw_fif(raw_fname, preload=True, verbose='error') raw.filter(1, 40, fir_design='firwin') # Set up events and epochs events = mne.find_events(raw, stim_channel='STI 014') event_id = {'aud/left': 1, 'aud/right': 2, 'vis/left': 3, 'vis/right': 4} t_min, t_max = -0.2, 0.5 epochs = mne.Epochs(raw, events, event_id, t_min, t_max, preload=True, verbose='error') # Compute evoked response evoked = epochs['aud/left'].average() # Plot evoked data evoked.plot() print(f"Processed data has {len(epochs)} epochs and {len(raw.ch_names)} channels.")

Debug

Known issues

breakingMNE-Python version 1.9.0 and later requires Python 3.10 or higher. Earlier Python versions are no longer supported.

fix

Upgrade your Python environment to 3.10 or newer.

affects: >=1.9.0

breakingAs of MNE-Python 1.9.0, the `mne.decoding` module explicitly requires `scikit-learn` to be installed.

fix

Ensure `scikit-learn` is installed: `pip install scikit-learn`.

affects: >=1.9.0

gotchaResampling raw data with `mne.io.Raw.resample()` before forming `Epochs` can cause event timing jitter, potentially impacting analyses sensitive to precise event timing.

fix

For final analysis, it is generally recommended to resample `Epochs` objects using `mne.Epochs.resample()` after epoching, or to be aware of the implications if resampling `Raw` data early.

affects: All versions

gotchaUsing mutable default arguments (e.g., lists, dictionaries) in custom functions that interact with MNE-Python objects can lead to unexpected shared state across function calls, a common Python pitfall.

fix

Initialize mutable defaults inside the function, e.g., `def func(arg=None): arg = arg if arg is not None else []`.

affects: All versions

gotchaIncorrectly identifying or handling 'bad' channels can bias results in subsequent processing steps like SSP (Signal Space Projection) or ICA (Independent Component Analysis).

fix

Carefully review channel quality, use `raw.plot(block=True)` for interactive marking, and utilize MNE-Python's tools for marking and interpolating bad channels (e.g., `raw.info['bads']`, `raw.interpolate_bads()`). Consider `autoreject` for automated detection.

affects: All versions

deprecatedThe `subject` parameter in `mne.datasets.eegbci.load_data()` was deprecated in favor of `subjects`.

fix

Use `subjects` instead of `subject` when calling `mne.datasets.eegbci.load_data()`.

affects: >=1.9.0

Upgrade

Version history

1.12.1latest on PyPI · released Apr 20, 2026

Audit

Dependencies

pythonrequiredRuntime environment

numpyrequiredCore numerical operations

scipyrequiredScientific computing tools

matplotlibrequired2D plotting

poochrequiredData fetching and caching

tqdmrequiredProgress bars

jinja2requiredTemplating for reports

decoratorrequiredUtility for decorators

lazy-loaderrequiredLazy loading of modules

packagingrequiredVersion handling

scikit-learnoptionalMachine learning, required for mne.decoding module

pyvistaoptional3D plotting capabilities

freesurferoptionalMRI processing and surface reconstruction (external software)

Agent activity

7 hits · last 30 days

node

Resources

githubhttps://github.com/mne-tools/mne-python ↗

packagehttps://pypi.org/project/mne/ ↗

homepagehttps://mne.tools/ ↗

docshttps://mne.tools/ ↗