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NatMEG Processing Pipeline

Comprehensive MEG/EEG preprocessing pipeline for NatMEG data including copying of data, MaxFilter processing, HPI coregistration, BIDS conversion, and data synchronization utilities.

Scripts and further information can be found at the NatMEG-utils GitHub repository

Overview

This pipeline provides end-to-end processing for:

  • TRIUX/SQUID MEG data from Elekta systems
  • OPM MEG data from Kaptah/OPM systems
  • EEG data collected through TRIUX

NatMEG Pipeline Overview

Pipeline overview showing the main components and data flow.

Key Features

  • GUI Configuration Interface: User-friendly GUI for setting up project parameters and pipeline options
  • Data Synchronization: Automated copying of raw data from SQUID/OPM computers to central processing computer copy_to_cerberos.py
  • HPI Coregistration: Automated head position indicator (HPI) coregistration for OPM-MEG data using Polhemus digitization add_hpi.py
  • Batch MaxFilter Processing: Integration with Elekta MaxFilter for Signal Space Separation (SSS) and temporal extension (tSSS) maxfilter.py
  • BIDS Conversion: Converts NatMEG data to BIDS format with customizable task and run mapping bidsify.py
  • Server Synchronization: Sync processed data to CIR server with advanced filtering options sync_to_cir.py
  • HTML Reporting: Generate interactive HTML reports summarizing processing steps and data status report.py
  • Logging and Error Handling: Comprehensive logging for tracking processing steps and troubleshooting
  • Other utilities: Additional scripts for specific tasks as needed

Configurations

Using the GUI

Alt text

Configuration GUI for setting up NatMEG pipeline parameters.

Configuration File details

General project paths and information. Make sure to set the correct paths for your data storage locations.

  • If using GUI project Name and Root will update Raw and BIDS paths if not manually changed.
  • Calibration and Crosstalk paths refer to in Project copies of these files, original locations are set in copy_to_cerberos.py script. This is used for BIDS conversion and MaxFilter processing.
  • Since project names can differ between Sinuhe/Kaptah and local storage, set the correct paths for your project on both systems by replacing the place holders. 
    Project:
  Name: ''
  CIR-ID: ''
  InstitutionName: Karolinska Institutet
  InstitutionAddress: Nobels vag 9, 171 77, Stockholm, Sweden
  InstitutionDepartmentName: Department of Clinical Neuroscience (CNS)
  Description: project for MEG data
  Tasks:
  - ''
  Sinuhe raw: /neuro/data/sinuhe/<project_path_on_sinuhe> 
  Kaptah raw: /neuro/data/kaptah/<project_path_on_kaptah>
  Root: /neuro/data/local/
  Raw: /neuro/data/local/<project>/raw
  BIDS: /neuro/data/local/<project>/BIDS
  Calibration: /neuro/data/local/<project>/databases/sss/sss_cal.dat
  Crosstalk: /neuro/data/local/<project>/databases/ctc/ct_sparse.fif
  Logfile: pipeline_log.log

OPM:
  polhemus:
  - ''
  hpi_names:
  - HPIpre
  - HPIpost
  - HPIbefore
  - HPIafter
  frequency: 33
  downsample_to_hz: 1000
  overwrite: false
  plot: false

Default settings.

  • Add all files for which you want continous head positioning estimation in trans_conditions
  • If you do not have empty room files, leave the list empty - ''
  • Add project bad channels in bad_channels list, one per line, or leave empty - '' 
    MaxFilter:
  standard_settings:
    trans_conditions:
    - ''
    trans_option: continous
    merge_runs: true
    empty_room_files:
    - empty_room_before.fif
    - empty_room_after.fif
    sss_files:
    - ''
    autobad: true
    badlimit: '7'
    bad_channels:
    - ''
    tsss_default: true
    correlation: '0.98'
    movecomp_default: true
    subjects_to_skip:
    - ''
  advanced_settings:
    force: false
    downsample: false
    downsample_factor: '4'
    apply_linefreq: false
    linefreq_Hz: '50'
    maxfilter_version: /neuro/bin/util/maxfilter
    MaxFilter_commands: ''
    debug: false

BIDS conversion settings. Make sure to set the correct paths for your files and project information. Example participant_mapping_example.csv.

  • The bids_conversion.tsv is generated if not already existing and controls the conversion. Edit task names and runs, and set status to ok. BIDS conversion will not be done if there are any file has status check.

  • A description.tsv file is also generated in the BIDS derivatives/preprocessing folder with descriptions of all processing tags.

BIDS:
  Dataset_description: dataset_description.json
  Participants: participants.tsv
  Participants_mapping_file: participant_mapping_example.csv
  Conversion_file: bids_conversion.tsv
  Overwrite_conversion: false
  Original_subjID_name: old_subject_id
  New_subjID_name: new_subject_id
  Original_session_name: old_session_id
  New_session_name: new_session_id
  overwrite: false
  dataset_type: raw
  data_license: ''
  authors: ''
  acknowledgements: ''
  how_to_acknowledge: ''
  funding: ''
  ethics_approvals: ''
  references_and_links: ''
  doi: doi:<insert_doi>

The steps to include in natmeg run --config <config_file.yml>, toggle between true/false to include/exclude steps. 

RUN:
  Copy to Cerberos: true
  Add HPI coregistration: true
  Run Maxfilter: true
  Run BIDS conversion: true
  Sync to CIR: true

Quick Start

The NatMEG pipeline includes an automated installation script that sets up everything you need. Install the pipeline using bash install.sh (see Manual installation section below for more details)

Default Installation (Conda Environment)

# Clone the repository
git clone https://github.com/NatMEG/NatMEG-utils.git
cd NatMEG-utils

# Run the installer (uses conda by default)
bash install.sh

# View all installer options
bash install.sh --help

Alternative Installation (Python Virtual Environment)

Note: Does not work on all Linux distributions (e.g. Rocky/RHEL/CentOS) due to PyQt compatibility issues. Use default conda installation instead.

# Clone the repository
git clone https://github.com/NatMEG/NatMEG-utils.git
cd NatMEG-utils

# Run the installer with venv flag
bash install.sh --venv
```**Benefits of conda installation (now default):**
- **Better PyQt Compatibility**: Provides isolated Python environment that avoids system conflicts
- **Linux Compatibility**: Works well on enterprise Linux distributions (Rocky/RHEL/CentOS)
- **Dependency Isolation**: Prevents conflicts with system-installed Python packages
- **Reliability**: More consistent installation experience across different systems

The installer will:
- Detect your operating system (macOS/Linux) and conda installation  
- Create a `natmeg` executable in `~/.local/bin/`
- Add `~/.local/bin` to your PATH if needed
- Set up either Python venv or conda environment based on your choice
- Provide clear troubleshooting instructions

After installation, you can use the pipeline from anywhere:
```bash
natmeg gui                     # Launch GUI
natmeg run --config config.yml # Run pipeline
natmeg --help                  # Show all options

Using the natmeg Command

After installation, you can use the natmeg command from anywhere:

# Launch GUI configuration interface
natmeg gui # Loads a default configuration file if none specified

# Create configuration file
natmeg create-config --output my_config.yml  # Generate default config

# Run complete pipeline with options
natmeg run --config config.yml               # Complete pipeline
natmeg run --config config.yml --dry-run     # Preview without execution
natmeg run --config config.yml --no-report   # Skip final HTML report
natmeg run --config config.yml --delete      # Delete remote files not in source

# Run individual components
natmeg copy --config config.yml              # Data synchronization only
natmeg hpi --config config.yml               # HPI coregistration only  
natmeg maxfilter --config config.yml         # MaxFilter processing only
natmeg maxfilter --config config.yml --dry-run  # Show commands without execution
natmeg bidsify --config config.yml           # BIDS conversion only

# Server synchronization with advanced options
natmeg sync --create-config                  # Generate example server config
natmeg sync --server-config servers.yml --test      # Test server connection
natmeg sync --directory /data/project        # Sync directory (default 'cir' server)  
natmeg sync --config project_config.yml --dry-run   # Preview sync from project config
natmeg sync --directory /data/project --delete      # Delete remote files not in source
natmeg sync --directory /data/project --exclude "*.tmp" --include "*.fif"  # Pattern filtering

# Generate project reports
natmeg report --config config.yml            # Generate HTML dashboard

# Show help for all commands
natmeg --help
natmeg run --help      # Detailed help for specific subcommand

Manual Installation

If you prefer manual setup:

Option 1: Conda Environment

# Create basic conda environment
conda create -n natmeg_utils python>=3.12 pip uv -y
conda activate natmeg_utils

# Install dependencies via pip (same as venv approach)
uv pip install -r requirements.txt

Option 2: Python Virtual Environment

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # or .venv\Scripts\activate on Windows

# Install dependencies
uv pip install -r requirements.txt

3. Add to PATH (Optional)

# Add repository to PATH for global access
echo 'export PATH="$HOME/Sites/NatMEG-utils:$PATH"' >> ~/.zshrc  # or ~/.bashrc
source ~/.zshrc

Prerequisites

  • Python 3.12+: Required for all pipeline components
  • Conda/Miniconda: Recommended for environment management
  • Git: For cloning the repository
  • Operating System: macOS or Linux (Windows support coming soon)

Troubleshooting

Installation Issues

PyQt Issues on Linux Rocky/RHEL: 

# Default conda installation should work out of the box
bash install.sh

# If you previously used venv and had issues, conda is now default
# Creates isolated Python environment avoiding system conflicts

Conda not found:

# Install conda first
brew install miniconda
# or download from: https://docs.conda.io/en/latest/miniconda.html

# Install conda first
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

natmeg command not found: 

# Check if ~/.local/bin is in PATH
echo $PATH

# If not, add it to your shell config
echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.zshrc  # or ~/.bashrc
source ~/.zshrc

Environment activation fails: 

# For conda environments (default)
conda env list
conda env remove -n natmeg_utils -y  # Remove corrupted environment
bash install.sh  # Recreate with conda (default)

# For virtual environments  
rm -rf .venv  # Remove corrupted environment
bash install.sh --venv  # Recreate with venv

Platform-Specific Issues

Linux Rocky/RHEL/CentOS - PyQt GUI Issues:

The GUI may fail to start on enterprise Linux distributions due to PyQt compatibility issues with system libraries. Solution: Use conda installation

# Recommended approach
bash install.sh --conda

# If already installed with pip/venv, switch to conda:
cd ~/Sites/NatMEG-utils
bash install.sh --conda  # This will replace the existing installation

Why conda is now the default: - Provides isolated Python environment separate from system Python - No conflicts with system-installed Python packages - Better compatibility across different operating systems and distributions - Same requirements.txt installation but in isolated conda environment - Resolves PyQt issues commonly seen with system Python installations

Runtime Issues

Module import errors: 

# For conda environments
conda activate natmeg_utils
conda list  # Check installed packages

# For virtual environments
source .venv/bin/activate
pip list  # Check installed packages

# Install missing dependencies
pip install mne mne-bids pyyaml

Permission errors: 

# Make natmeg executable
chmod +x ~/.local/bin/natmeg

# Check file permissions
ls -la ~/.local/bin/natmeg

Terminal crashes: The natmeg script includes safety checks to prevent terminal crashes. If issues persist: 

# View the generated script
cat ~/.local/bin/natmeg

# Regenerate with latest safety checks
cd ~/Sites/NatMEG-utils
bash install.sh

Contributions

Improvements are welcomed! The pipeline includes robust installation and execution scripts that work across different environments.

Development Guidelines: - Do not change scripts locally for personal use - Follow GitHub conventions: create branches or fork the repository - Make pull requests for any modifications - Test installation script on both macOS and Linux before submitting changes - Ensure compatibility with different conda installations and shell environments

Testing the Installation: 

# Test on clean environment
bash install.sh

# Verify functionality
natmeg --help
natmeg gui