dicomwf 0.3.0

A Python library for creating DICOM EEG waveform files

dicomwf

A Python library for integrating waveforms in DICOM files.

Features

Supported waveforms IODs:

• RoutineScalpEEG

Installation

User Installation

Install directly from PyPI:

pip install dicomwf

Developer Installation

1. Clone the repository:

git clone https://gitlab.inria.fr/openvibe/dicomwf.git
cd dicomwf

2. Create and activate a virtual environment:

python -m venv .venv

# On Windows
.venv\Scripts\activate

# On Unix/macOS
source .venv/bin/activate

3. Install in editable mode with development dependencies:

pip install -e ".[dev]"

This installs the package in editable mode along with development tools.

Quick Start

We will use the EEG data in the edf format from the EEGBCI dataset.

import numpy as np
from dicomwf import *
from urllib.request import urlretrieve
import mne

# Load EEG data
url = "https://physionet.org/files/eegmmidb/1.0.0/S001/S001R01.edf?download"
raw_fname = "S001R01.edf"
urlretrieve(url, raw_fname)

raw = mne.io.read_raw_edf(raw_fname, preload=True)

# Create DICOM EEG
dicom_eeg_data = RoutineScalpEEGData(
    patient=Patient(name="Doe^John", birth_date="20250101", sex="M"),
    study=GeneralStudy(id="1",date="19940423", time="101601.934000", accession_number="1"),
    equipment=GeneralEquipment(
        manufacturer="PyDICOM",
        manufacturer_model_name="PyDICOM",
        device_serial_number="1",
        software_versions="1.0",
    ),
    waveform_identification=WaveformIdentification(),
    waveform=Waveform(
        channel_count=raw.info["nchan"],
        sampling_frequency=raw.info["sfreq"],
        channel_names=raw.ch_names,
        data=np.transpose(raw.get_data()),
    ),
)

dicom_eeg = RoutineScalpEEG(uid_root="1.2.3.4.5.", rse_data=dicom_eeg_data)
dicom_eeg.save_as("output.dcm")

Dependencies

Required:

• numpy >= 1.20.0
• pydicom >= 2.3.0

Optional (for development):

• pytest >= 7.0.0
• mne>=1.11.0

Running Tests

pytest

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Authors

• Evgenia Kartsaki
• Thomas Prampart
• Michael Kain
• Eric Poiseau
• Clement Chesnin

Data Sources

This project uses the following data for unit testing:

• [EEG Motor Movement/Imagery Dataset]: Sourced from [https://physionet.org/content/eegmmidb/1.0.0/S001/].
• License: This data is licensed under the Open Data Commons Attribution License (ODC-By) v1.0.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.