py-neuromodulation 0.0.7

Real-time analysis of intracranial neurophysiology recordings.

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 3.10. For installation you can use pip:

pip install py-neuromodulation

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

uv python install 3.10
uv venv
. .venv/bin/activate
uv sync

Then py_neuromodulation can be imported via:

import py_neuromodulation as nm

Basic Usage

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.

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).