https://github.com/alexrockhill/bpc_jupyter

Jupyter notebook for the basis profile curves

https://github.com/alexrockhill/bpc_jupyter

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Jupyter notebook for the basis profile curves

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  • Host: GitHub
  • Owner: alexrockhill
  • License: gpl-3.0
  • Default Branch: main
  • Size: 6.77 MB
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Fork of MultimodalNeuroimagingLab/bpc_jupyter
Created almost 3 years ago · Last pushed almost 3 years ago
Metadata Files
Readme License

README.md

bpc_jupyter

This Jupyter notebook is a tutorial for how to calculate Basis Profile Curve for intracranial EEG data during single pulse stimulation as described in:

Miller KJ, Müller K-R, Hermes D (2021) Basis profile curve identification to understand electrical stimulation effects in human brain networks. PLoS Comput Biol 17(9): e1008710. https://doi.org/10.1371/journal.pcbi.1008710

Please cite this work when using the code.

This Jupyter notebook is a translation of the Matlab code that was developed by Kai Miller in the paper. The Jupyter notebook was written by Tal Pal Attia, Harvey Huang and Dora Hermes, 2021.

Preparations

There are two steps to prepare for the tutorial:

  1. Download this example dataset from OpenNeuro: https://openneuro.org/datasets/ds003708

  2. Set up the environment and add the required python packages by opening the jupiter notebook (bpc_interactive.ipynb) and completing Section 1, Getting started with the Python environment and packages

Acknowledgements

This project was funded by the National Institute Of Mental Health of the National Institutes of Health under Award Number R01MH122258.

Owner

  • Name: Alex Rockhill
  • Login: alexrockhill
  • Kind: user
  • Location: Eugene, OR
  • Company: University of Oregon

Graduate Student in the Swann Lab

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Dependencies

bpc-requirements.txt pypi
  • ipykernel *
  • matplotlib *
  • nibabel *
  • nilearn *
  • numpy >=1.24.4
  • pandas >=2.0.3
  • pybids *
  • pymef ==1.3.4
  • scipy >=1.10.1
  • sklearn *
  • tqdm *