pybemobil

MoBI data loading, preprocessing, and visualization built on MNE-Python.

Structure

pybemobil/
├── io/                          # Data I/O
│   ├── xdf.py                   # XDFLoader: multimodal XDF loading, stream alignment
│   ├── alignment.py             # align_stream_to_timestamps (timestamp-aware interpolation)
│   ├── bids_export.py           # BIDS export wrappers around mne_bids
│   └── utils.py                 # I/O helpers
├── preproc/                     # Preprocessing
│   ├── preprocessing.py         # EEGPreprocessor: ZapLine -> bad ch -> filter -> ASR -> ICA pipeline
│   ├── epoching.py              # EpochPreparer, stimulus renaming helpers
│   ├── motion.py                # Rigid body kinematics (find, process, split)
│   └── utils.py                 # ASR, ZapLine, ICA, dipole fitting, provenance, misc
├── viz/                         # Visualization
│   ├── plotting.py              # ERP, PSD, and component plots
│   └── utils.py                 # Plot helpers
├── examples/                    # Runnable example scripts
│   ├── preprocessing.py         # Full EEG preprocessing walkthrough
│   ├── plotting.py               # Visualization walkthrough (run preprocessing.py first)
│   ├── xdf_loading.py           # XDF multimodal loading
│   ├── motion_processing.py     # Rigid body kinematics
│   └── bids_export_example.py   # BIDS export
└── data/
    └── montages/                # EEG electrode montage files (.elc, .fif)

Modules

io - Load XDF multimodal recordings into MNE Raw objects, align auxiliary streams (ECG, gaze, EMG, EDA) to a common time grid, and export datasets to BIDS.

preproc - Full EEG preprocessing pipeline (bandpass, bad channel detection, ASR, ICA, dipole fitting), epoch preparation, and rigid body motion capture kinematics.

viz - ERP and PSD plotting utilities built on MNE and matplotlib.

examples - Self-contained scripts demonstrating major features. Run directly or open as notebooks (percent-format cells).

Installation

Install the latest release from PyPI:

pip install pybemobil

To also install the JAX-accelerated AMICA backend (the default ICA method, which falls back to Picard when this is missing):

pip install "pybemobil[jamica]"

Alternatively:

• Latest development version from GitHub:

  pip install git+https://github.com/BeMoBIL/pybemobil.git
  

• From source, in editable mode (for development):

  git clone https://github.com/BeMoBIL/pybemobil.git
  cd pybemobil
  pip install -e ".[dev]"
  

We recommend installing into a fresh environment, e.g. conda create -n pybemobil python=3.12.

Quick start

from pybemobil.io import XDFLoader
from pybemobil.preproc import EEGPreprocessor, find_rigid_bodies

# Load a multimodal XDF recording
loader = XDFLoader(montage="easycap-M1")  # or a path to your .elc/.fif
recording = loader.load("sub-01_task-walk_run-01.xdf")
raw = recording.raw

See examples/ for complete worked examples:

Citing

If you use pybemobil in your work, please cite it. The "Cite this repository" button on GitHub (generated from CITATION.cff) gives the reference in APA and BibTeX format.

pybemobil is a Python implementation of the BeMoBIL pipeline, so please consider also citing:

Klug, M., Jeung, S., Wunderlich, A., Gehrke, L., Protzak, J., Djebbara, Z., Argubi-Wollesen, A., Wollesen, B., & Gramann, K. (2022). The BeMoBIL Pipeline for automated analyses of multimodal mobile brain and body imaging data. bioRxiv. https://doi.org/10.1101/2022.09.29.510051

License

pybemobil is released under the BSD 3-Clause License.

Contributing

Contributions are welcome! To report a bug or suggest a feature, please open an issue. For development setup, the pull request workflow, testing and releases, see CONTRIBUTING.md.