Welcome to py_neuromodulation’s documentation!#
The py_neuromodulation toolbox allows for real time capable feature estimation of invasive electrophysiological data.
Contents
Why 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 features and includes commonly applied pre -and postprocessing methods.
Basically only time series data with a corresponding sampling frequency are required.
The output will be a pandas DataFrame including different time-resolved computed features. Internally a stream get’s initialized, which simulates an online data-stream that can also be be used for real-time analysis.
The following features are currently included:
oscillatory: fft, stft or bandpass filtered band power
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].
How can those features be used?#
The original intention for writing this toolbox was movement decoding from invasive brain signals [2]. The application however could be any neural decoding and analysis problem. py_neuromodulation offers wrappers around common practice machine learning methods for efficient analysis.