mne-bids
MNE-BIDS: Organizing MEG, EEG, and iEEG data according to the BIDS specification and facilitating their analysis with MNE-Python
Decision gist · record as of 2026-08-14
Yes. MNE-BIDS is actively maintained, has low install friction, carries a permissive license, and solves a real problem for neuroimaging researchers: standardizing electrophysiology data organization. If you work with MEG, EEG, or iEEG data and use MNE-Python, this package is the standard way to organize and share your data. No known security issues.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires MNE-Python and its dependencies (numpy, scipy) to be installed; MNE itself may require additional system libraries for data I/O.
- Low friction: pure Python wheel with only three runtime dependencies (mne, numpy, scipy).
- Active maintenance with a release 80 days ago and ongoing commits.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause is permissive; you can use, modify, and distribute this package with minimal restrictions, provided you retain the license notice.
last release 2026-05-26 (80 days) · last repo commit 2026-08-10 · 180 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 119,618 downloads/mo, #12,060 on PyPI
Alternatives
Verify before relying
pip install mne-bids
import mne_bids
from mne_bids import write_raw_bids
# write_raw_bids(raw, bids_path, overwrite=True)- Whether the package handles all BIDS data types (MEG, EEG, iEEG, NIRS, Motion) equally well or if some have better support.
- Performance characteristics when working with large datasets or many subjects.
- Compatibility with the mne-bids-pipeline mentioned in the description.
What it is and what it does
MNE-BIDS bridges MNE-Python and the Brain Imaging Data Structure (BIDS) standard, allowing you to read and write neuroimaging datasets in a standardized format. It handles MEG, EEG, and iEEG data, making it easier to organize, share, and analyze electrophysiological recordings. The package depends on MNE-Python, numpy, and scipy, so you need the full MNE stack installed.
The primary goal is to reduce coding friction and improve reproducibility by standardizing how neuroimaging data is stored and organized. BIDS format also enables integration with automated analysis pipelines and facilitates collaboration by ensuring your data follows a widely-recognized specification. It's particularly useful if you're working in a lab setting where data sharing and pipeline reuse are common.
Use it for
- Convert raw MEG, EEG, or iEEG recordings into BIDS-compliant directory structures for archival and sharing.
- Read BIDS-formatted datasets into MNE-Python for analysis without manual file organization.
- Prepare data for automated neuroimaging pipelines that expect BIDS input.
- Standardize electrophysiology data collection across multiple subjects or sessions in a research study.
- Facilitate reproducible research by ensuring collaborators receive data in a consistent, well-documented format.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
MNE-BIDS is actively maintained, has low install friction, carries a permissive license, and solves a real problem for neuroimaging researchers: standardizing electrophysiology data organization. If you work with MEG, EEG, or iEEG data and use MNE-Python, this package is the standard way to organize and share your data. No known security issues.
Install
mne-bids on PyPI
Before you install
Low friction: pure Python wheel with only three runtime dependencies (mne, numpy, scipy). Active maintenance with a release 80 days ago and ongoing commits. Supports current Python versions (3.11–3.14).
Requires MNE-Python and its dependencies (numpy, scipy) to be installed; MNE itself may require additional system libraries for data I/O.
License in practice
BSD-3-Clause is permissive; you can use, modify, and distribute this package with minimal restrictions, provided you retain the license notice.
Quickstart
pip install mne-bids
import mne_bids
from mne_bids import write_raw_bids
# write_raw_bids(raw, bids_path, overwrite=True)Verify before relying
- Whether the package handles all BIDS data types (MEG, EEG, iEEG, NIRS, Motion) equally well or if some have better support.
- Performance characteristics when working with large datasets or many subjects.
- Compatibility with the mne-bids-pipeline mentioned in the description.
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesmnenumpyscipy |
| Maintenance | Actively maintained 80 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 119,618 / month, #12,060 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 :: POSIX :: LinuxProgramming Language :: PythonProgramming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Software Development |
Evidence: mne_bids-0.19.0-py3-none-any.whl
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See also mne · neo · pybids · bidsschematools · nilearn · bids-validator · nipype · dipy · pyxnat · pynwb