meg2bids

Your friendly MEG to BIDS converter.

meg2bids reorganizes MEG FIF files into the Brain Imaging Data Structure (BIDS) format using MNE-Python and mne-bids.

🎯 Scope

meg2bids is designed specifically for Neuromag/Elekta/MEGIN MEG systems that produce FIF (.fif) files. It handles:

• ✅ Raw MEG FIF files conversion to BIDS
• ✅ Simultaneous MEG/EEG recording support with automatic electrodes.tsv generation
• ✅ Automatic MaxFilter derivative detection and organization
• ✅ Split file support (files > 2GB)
• ✅ Calibration file management (crosstalk and fine-calibration)
• ✅ Session auto-detection (including legacy format — FIF files directly in subject folder)
• ✅ Multi-subject batch processing
• ✅ File exclusion via wildcard patterns
• ✅ FIF header deduplication (removes duplicate recordings automatically)
• ✅ Acquisition label (acq) support in file patterns

Note: Currently supports only FIF format from Neuromag/Elekta/MEGIN systems. Support for other MEG manufacturers (CTF, BTI/4D, KIT/Yokogawa) may be added in future releases.

📋 Requirements

• Python 3.8 or higher
• MNE-Python >= 1.0
• mne-bids >= 0.13

🚀 Installation

From PyPI (when published)

pip install meg2bids

From source

git clone https://github.com/ln2t/meg2bids.git
cd meg2bids
pip install -e .

Using conda/mamba

conda create -n meg2bids python=3.10
conda activate meg2bids
pip install -e .

📖 Quick Start

1. Organize Your Data

Your source data should follow this structure:

sourcedata/
<dataset>-sourcedata/
meg/
meg_XXXX/ # MEG subject folders
YYMMDD/ # Session date folders
*.fif # MEG FIF files
configs/
meg2bids.json # Configuration file
participants_complete.tsv # Subject mapping

2. Create Configuration File

Create meg2bids.json in your configs directory:

{
"dataset": {
"dataset_name": "MyStudy",
"datatype": "meg"
},
"file_patterns": [
{
"pattern": "*rest*.fif",
"task": "rest",
"run_extraction": "last_digits",
"description": "Resting state"
},
{
"pattern": "*chessboard*.fif",
"task": "visual",
"run_extraction": "last_digits",
"description": "Visual task"
}
],
"calibration": {
"system": "triux",
"auto_detect": true,
"maxfilter_root": "MEG/maxfilter"
},
"derivatives": {
"pipeline_name": "maxfilter",
"maxfilter_version": "v2.2.20"
},
"options": {
"allow_maxshield": true,
"overwrite": true
},
"exclude_patterns": [
"*empty_room*",
"*noise*"
]
}

3. Create Participants Mapping

Create participants_complete.tsv:

participant_idmeg_idsub-01	1001
sub-02	1002

4. Run Conversion

# Check configuration first
python meg2bids.py --dataset mydataset --check-config
# Convert all subjects
python meg2bids.py --dataset mydataset
# Convert single subject
python meg2bids.py --dataset mydataset --subject sub-01
# With BIDS validation
python meg2bids.py --dataset mydataset --subject sub-01 --validate

📚 Documentation

• Installation Guide
• Configuration Guide
• Tutorial: First Steps
• Advanced Usage
• API Reference

🔧 Key Features

Automatic Derivative Detection

meg2bids automatically detects MaxFilter derivatives by recognizing standard suffixes:

• _sss, _tsss → Signal Space Separation
• _mc → Movement compensation
• _trans, _quat → Head position
• _av, _ave → Averaged data

Example: restingstate_mc_ave.fif → sub-01_task-rest_proc-mc-ave_meg.fif

Calibration File Management

Automatically detects and copies calibration files:

• Crosstalk: ct_sparse_triux2.fif (Triux) or ct_sparse_erasme_enm.fif (VectorView)
• Fine-calibration: sss_cal_XXXX_*.dat (date-matched to session) or sss_cal_erasme_enm.dat (VectorView)

The session date is extracted from the session folder name (YYMMDD format). If no date is found in the folder name, it falls back to reading the measurement date from the FIF header.

Split File Handling

Handles large files automatically split by the acquisition system:

• filename.fif, filename-1.fif, filename-2.fif → Single BIDS entry

Derivative split files are also handled:

• Pattern 1 (split before proc): file.fif, file-1_sss.fif, file-2_sss.fif
• Pattern 2 (split after proc): file_sss-1.fif, file_sss-2.fif

File Exclusion

Exclude specific files using wildcard patterns in the config:

"exclude_patterns": ["*empty_room*", "*noise*", "*test*"]

Excluded files are reported in the conversion summary with an ⊗ Excluded marker.

FIF Header Deduplication

When the same recording is stored multiple times (e.g., backup copies), meg2bids automatically detects duplicates by comparing FIF header fingerprints and keeps only one copy per recording.

🗂️ Output Structure

rawdata/
<dataset>-rawdata/
sub-01/
ses-01/
meg/
sub-01_ses-01_task-rest_meg.fif
sub-01_ses-01_task-rest_meg.json
sub-01_ses-01_task-rest_channels.tsv
sub-01_ses-01_acq-crosstalk_meg.fif
sub-01_ses-01_acq-calibration_meg.dat
derivatives/
<dataset>-derivatives/
maxfilter_v2.2.20/
sub-01/
ses-01/
meg/
sub-01_ses-01_task-rest_proc-sss_meg.fif

🛠️ Command-Line Interface

python meg2bids.py --dataset DATASET [OPTIONS]
Required Arguments:
--dataset DATASET Dataset name (e.g., 'mystudy')
Optional Arguments:
--subject SUBJECT Process single subject (sub-01, 01, or meg_id)
-b, --validate Run BIDS validation after conversion
--check-config Validate config without conversion
Paths (auto-constructed from dataset):
sourcedata/<dataset>-sourcedata/meg/
rawdata/<dataset>-rawdata/
derivatives/<dataset>-derivatives/

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Development Setup

git clone https://github.com/ln2t/meg2bids.git
cd meg2bids
pip install -e ".[dev]"
pytest tests/

📄 License

This project is licensed under the GNU General Public License v3.0 - see LICENSE for details.

📖 Citation

If you use meg2bids in your research, please cite:

@software{meg2bids,
author = {Your Name},
title = {meg2bids: MEG to BIDS Converter},
year = {2026},
url = {https://github.com/ln2t/meg2bids}
}

🙏 Acknowledgments

• Inspired by dcm2bids
• Built with MNE-Python and mne-bids
• Follows BIDS specification

❓ Issues and Questions

• Usage questions: Post on Neurostars with the meg2bids tag
• Bug reports: Open an issue on GitHub
• Feature requests: Open an issue with the enhancement label

🔗 Related Projects

• dcm2bids - DICOM to BIDS converter
• mne-bids - MNE-Python BIDS integration
• bidscoin - Multi-modal BIDS converter