https://github.com/neuromodulation/py_neuromodulation

Real-time analysis of intracranial neurophysiology recordings.
https://github.com/neuromodulation/py_neuromodulation

dbs deep-brain-stimulation ecog electrocorticography machine-learning python real-time

Last synced: 4 months ago
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Real-time analysis of intracranial neurophysiology recordings.

• Host: GitHub
• URL: https://github.com/neuromodulation/py_neuromodulation
• Owner: neuromodulation
• License: mit
• Created: 2021-03-03T11:35:28.000Z (over 5 years ago)
• Default Branch: main
• Last Pushed: 2025-11-04T10:40:01.000Z (8 months ago)
• Last Synced: 2026-01-28T10:49:58.677Z (5 months ago)
• Topics: dbs, deep-brain-stimulation, ecog, electrocorticography, machine-learning, python, real-time
• Language: Python
• Homepage: https://neuromodulation.github.io/py_neuromodulation/
• Size: 332 MB
• Stars: 70
• Watchers: 3
• Forks: 19
• Open Issues: 3
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

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README

          


py_neuromodulation

==================

Journal of Open Source Science publication:

.. image:: https://joss.theoj.org/papers/10.21105/joss.08258/status.svg

   :target: https://doi.org/10.21105/joss.08258

Documentation: https://neuromodulation.github.io/py_neuromodulation/

Analyzing neural data can be a troublesome, trial and error prone,

and beginner unfriendly process. *py_neuromodulation* allows using a simple

interface for extraction of established neurophysiological features and includes commonly applied pre -and postprocessing methods.

Only **time series data** with a corresponding **sampling frequency** are required for feature extraction.

The output will be a `pandas.DataFrame `_ including different time-resolved computed features. Internally a **stream** get's initialized,

which resembles an *online* data-stream that can in theory also be be used with a hardware acquisition system. 

The following features are currently included:

* oscillatory: fft, stft or bandpass filtered band power

* `temporal waveform shape `_

* `fooof `_

* `mne_connectivity estimates `_ 

* `Hjorth parameter `_

* `non-linear dynamical estimates `_

* various burst features

* line length 

* and more...

Find here the preprint of **py_neuromodulation** called *"Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants"* [1]_.

The original intention for writing this toolbox was movement decoding from invasive brain signals [2]_.

The application however could be any neural decoding problem.

*py_neuromodulation* offers wrappers around common practice machine learning methods for efficient analysis.

Find the documentation here neuromodulation.github.io/py_neuromodulation/ for example usage and parametrization.

Installation

============

py_neuromodulation requires at least python 3.12. For installation you can use pip:

.. code-block::

    pip install py-neuromodulation

Alternatively you can also clone the pacakge and install it using `uv `_:

.. code-block::

    uv python install 3.12

    uv venv

    . .venv/bin/activate

    uv sync

Then *py_neuromodulation* can be imported via:

.. code-block::

    import py_neuromodulation as nm

Basic Usage

===========

.. code-block:: python

    

    import py_neuromodulation as nm

    import numpy as np

    

    NUM_CHANNELS = 5

    NUM_DATA = 10000

    sfreq = 1000  # Hz

    sampling_rate_features_hz = 3  # Hz

    data = np.random.random([NUM_CHANNELS, NUM_DATA])

    stream = nm.Stream(sfreq=sfreq, data=data, sampling_rate_features_hz=sampling_rate_features_hz)

    features = stream.run()

Check the `Usage `_ and `First examples `_ for further introduction.

Contact information

-------------------

For any question or suggestion please find my contact

information at `my GitHub profile `_.

Contributing guide

------------------

https://neuromodulation.github.io/py_neuromodulation/contributing.html

References

----------

.. [1] Merk, T. et al. *Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants*, `https://doi.org/10.21203/rs.3.rs-3212709/v1` (2023).

.. [2] Merk, T. et al. *Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson’s disease*. Elife 11, e75126, `https://doi.org/10.7554/eLife.75126` (2022).