mef3io

Read and write MEF 3.0 (Multiscale Electrophysiology Format) files from Python, MATLAB and C++. MEF 3.0 is a compressed, optionally encrypted file format for long electrophysiology recordings: mainly EEG, intracranial EEG (iEEG) and ECG, but it suits any regularly sampled signal, such as EMG, other biosignals or sensor data. One C++ core does the work, so every language reads and writes the same files the same way. Sessions written by mef3io open in the established MEF 3.0 tools listed below, and sessions written by those tools open in mef3io.

Documentation: https://bnelair.github.io/mef3io/

MEF 3.0 and the original libraries

mef3io grew out of an earlier stack of MEF libraries and stays compatible with it: files written by either open in the other. MEF 3.0 support is maintained here, in mef3io, as open source (Apache-2.0).

• MEF 3.0 specification: the header of meflib, meflib.h, is titled "Specification for Multiscale Electrophysiology Format (MEF) version 3.0". The format's design is described in Brinkmann et al., Large-scale electrophysiology: acquisition, compression, encryption, and storage of big data, J Neurosci Methods 180(1):185-192, 2009, doi:10.1016/j.jneumeth.2009.03.022.
• meflib: the original reference C library for MEF 3.0, from the msel-source project.
• pymef (docs): a Python wrapper over meflib.
• mef_tools (archived; on PyPI as mef-tools): high-level Python reading and writing built on pymef. mef3io stands in for it: from mef3io import MefReader, MefWriter.
• MATLAB (legacy): Richard Jie Cui's MEF_import (EEGLAB plugin) and mef_reader_fieldtrip (FieldTrip reader for MEF 2.1, MEF 3.0 and MED 1.0). mef3io has its own MATLAB interface.
• The older MEF 2.1 format has its own library and specification, mef_lib_2_1; mef3io reads and writes MEF 3.0 only.

mef3io is an independent implementation of the specification, tested against meflib, pymef and mef_tools. New to the format? See MEF 3.0 for students.

Install

pip install mef3io

Prebuilt wheels for Linux, macOS and Windows (Python 3.10+); numpy is the only dependency. For MATLAB, download mef3io-matlab-vX.Y.Z.zip from the latest release and addpath it, or build the MEX yourself. For C++, add core/ to your CMake project. Details for all three: Install.

Usage

import numpy as np
import mef3io

fs = 256.0
start = 1_700_000_000_000_000            # microseconds since 1970 (uUTC)
signal = np.random.randn(60 * int(fs))   # one minute
signal[1000:1500] = np.nan               # a gap: NaN is stored as missing data

with mef3io.Writer("session.mefd", overwrite=True, units="uV") as w:
    w.write("eeg1", signal, start, fs=fs)
    w.write_annotations([{"time": start, "text": "recording start"}], channel="eeg1")

with mef3io.Reader("session.mefd") as r:
    print(r.channels)                     # ['eeg1']
    x = r.read("eeg1", start, start + 10_000_000)  # first 10 s, float64, gaps = NaN
    print(r.segments("eeg1"))             # what data is where
    print(r.records("eeg1"))              # annotations

The same session in MATLAB:

r = mef3io.Reader('session.mefd');
x = r.read('eeg1');

More: the Python, MATLAB and C++ guides, runnable examples, and short how-to pages such as converting EDF, EDF+ or BDF to MEF 3.0.

What it is built for

• Recordings far larger than memory. A single channel of a long recording can be tens of gigabytes. A read loads only the blocks the requested time window needs, and decoding runs on all cores.
• Missing data as a first-class value. Gaps in a recording come back as NaN on the sampling grid, never silently stitched together; read_raw returns the raw integer samples with a validity mask.
• Recordings that keep growing. A session can be appended to for days or months; with one Writer kept open, each append costs time in proportion to the new data, not to the recording so far. If a write is interrupted, the session can be checked and recovered — see Long recordings and Validating and repairing.
• Existing data and code. It reads MEF 3.0 sessions from other writers, and from mef3io import MefReader, MefWriter runs code written for the legacy mef_tools package with only the import changed.
• Protected recordings. Both MEF 3.0 password levels work as specified: the level-1 password opens the technical metadata while the subject's identity stays behind the level-2 password.
• Looking before reading. mef3io.inspect(path) (or python -m mef3io inspect) describes a session — channels, segments, gaps, annotations, encryption, what identifying fields it holds — without reading any samples, and long reads and operations can report progress and be cancelled from another thread (Python and C++).
• One file instead of a folder. A session can be packed into a single .mefd.tar and read in place, without unpacking.

Upgrading from mef3io 1.1.2 or earlier

Sessions written by mef3io 1.1.2 and earlier read correctly in mef3io and pymef, but declare some buffer sizes as 0, which can crash meflib-based readers. Opening one warns once. Appending with a current version repairs the segment it extends; mef3io.repair_session fixes the rest in place — see Validating and repairing. Changes in every version, and what an upgrade asks of you, are listed in the release notes.

Scope and limitations

• Video files (.vidd) are out of scope; a session's video folders are skipped.
• Encryption is all or nothing per session: unencrypted, or both password levels set. It protects the subject's identity and annotation text, not the signal: the samples and their timing can be decoded without a password, so keep confidential recordings on protected storage. See what encryption protects.
• Data-block encryption. The MEF 3.0 specification defines encrypted data blocks. Leaving them unencrypted is a habit of past implementations, not the format's design: meflib does not encrypt them unless asked, and the legacy readers mishandle encrypted blocks (pymef crashes on them, and meflib requires the level-2 password even for level-1 blocks). mef3io reads such blocks (some third-party tools write them): a block encrypted at level 1 with either password, one at level 2 with the level-2 password. Recordings mef3io writes today leave data blocks unencrypted and stay fully compatible; encrypted metadata works in every direction. mef3io intends to follow the specification here later, which could produce recordings that older tools cannot read; that is not decided yet (#94, #92).
• Single-file .mefd.tar sessions are a mef3io addition, not part of MEF 3.0, so meflib, pymef and mef_tools cannot open them directly. They are plain uncompressed tar files, so they can always be unpacked: mef3io.extract_session, tar -xf (built into Windows 10 and later, macOS and Linux) or the free 7-Zip on Windows. The unpacked .mefd folder works with the legacy tools.
• Records (annotations): every record type MEF 3.0 defines is read; Note, SyLg and EDFA can be written with their text.
• De-identification: meflib's recording-time offset does not hide the recording date (it stays in every metadata file, in plain text unless the session is encrypted), so mef3io never creates such sessions; it reads and extends existing ones. A real de-identified copy is planned (#70) — see De-identification.
• Pure-Python backend is not implemented (backend="pure" raises); the wheels carry the compiled core.

Contributing and support

Bug reports, feature requests and questions go to issues. To build and test from source, see CONTRIBUTING.md; the on-disk format reference and the design are on the documentation site. Report vulnerabilities privately as described in SECURITY.md.

core/        C++ library (the format, read, write, validation) + Catch2 tests
bindings/    Python extension (nanobind)
python/      the mef3io Python package
matlab/      MEX gateway over the C ABI + the +mef3io MATLAB classes
examples/    runnable scripts
tests/       pytest suites, cross-checked against pymef / mef_tools
benchmarks/  performance comparisons
docs/        documentation site source

Related projects

• mef3io-studio: a desktop app for exploring and anonymizing MEF 3.0 recordings; installers are on its Releases page.
• mef3io-server: a gRPC server that streams MEF 3.0 data to viewers on a local network.

Citing and license

If you use mef3io in published work, please cite it via CITATION.cff. Licensed under Apache 2.0. Maintained by Filip Mivalt, Bioelectronics Neurophysiology and Engineering Laboratory (BNEL) at Mayo Clinic.

Contributors and credits

mef3io: Filip Mivalt (maintainer), Vladimir Sladky.

The format and the earlier tools, whose authors we thank for making this work possible: Matt Stead, who conceived MEF, and his co-authors of the 2009 paper Benjamin H. Brinkmann, Mark R. Bower, Keith A. Stengel and Gregory A. Worrell; the meflib, pymef and mef_tools contributors, including Jan Cimbálník, Daniel P. Crepeau, Max van den Boom, Ben Brinkmann, Eric Larson, Václav Křemen and others; and Richard Jie Cui for the MATLAB tools. The full list, with what each project did, is in CONTRIBUTORS.md.

This project follows a code of conduct.