Metadata-Version: 2.1
Name: pynibs
Version: 0.2023.3
Summary: A python toolbox to conduct non-invasive brain stimulation experiments (NIBS).
Author: Konstantin Weise, Ole Numssen, Benjamin Kalloch
Author-email: kweise@cbs.mpg.de
License: GPL3
Project-URL: Home, https://gitlab.gwdg.de/tms-localization/pynibs
Project-URL: Docs, https://pynibs.readthedocs.io/
Project-URL: Twitter, https://www.twitter.com/num_ole
Project-URL: Download, https://pypi.org/project/pynibs/
Keywords: NIBS,non-invasive brain stimulation,TMS,FEM
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Build Tools
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

# pyNIBS
Preprocessing, postprocessing, and analyses routines for non-invasive brain stimulation experiments.

[![Latest Release](https://gitlab.gwdg.de/tms-localization/pynibs/-/badges/release.svg)](https://gitlab.gwdg.de/tms-localization/pynibs)
[![Documentation](https://readthedocs.org/projects/pynibs/badge/)](https://pynibs.readthedocs.io/)
[![pipeline status](https://gitlab.gwdg.de/tms-localization/pynibs/badges/master/pipeline.svg)](https://gitlab.gwdg.de/tms-localization/pynibs/commits/master)
[![coverage report](https://gitlab.gwdg.de/tms-localization/pynibs/badges/master/coverage.svg)](https://tms-localization.pages.gwdg.de/pynibs)

![](https://gitlab.gwdg.de/uploads/-/system/project/avatar/9753/Fig_4.png?width=128)

`pyNIBS` provides the functions to allow **cortical mappings** with transcranial magnetic stimulation (TMS) via **functional analysis**. `pyNIBS` is developed to work with [SimNIBS](http://www.simnibs.org), i.e. SimNIBS' meshes and FEM results can directly be used.
 Currently, [SimNIBS 3.2.6](https://github.com/simnibs/simnibs/releases/tag/v3.2.6) and [SimNIBS 4.0](https://github.com/simnibs/simnibs/releases/tag/v4.0.0) is supported.

See the [documentation](https://pynibs.readthedocs.io/) for package details and our [protocol](https://doi.org/10.1038/s41596-022-00776-6) publication for a extensive usage examples. Free view only version of the paper: https://t.co/uv7CmVw6tp.

## Installation
Via pip:

``` bash
pip install pynibs
```

Or clone the source repository and install via `setup.py`:

``` bash
git clone https://gitlab.gwdg.de/tms-localization/pynibs
cd pynibs
python setup.py develop
```

To import CED Signal EMG data use the `export to .mat` feature of Signal. 
To read `.cfs` files exported with CED Signal you might need to [manually](HOW_TO_INSTALL_BIOSIG.txt) compile the libbiosig package.


## Bugs
For sure. Please open an [issue](https://gitlab.gwdg.de/tms-localization/pynibs/-/issues) or feel free to file a PR.


## Citation
Please cite _Numssen, O., Zier, A. L., Thielscher, A., Hartwigsen, G., Knösche, T. R., & Weise, K. (2021). Efficient high-resolution TMS mapping of the human motor cortex by nonlinear regression. NeuroImage, 245, 118654. doi:[10.1016/j.neuroimage.2021.118654](https://doi.org/10.1016/j.neuroimage.2021.118654)_ when using this toolbox in your research.


## References
  - Weise*, K., Numssen*, O., Thielscher, A., Hartwigsen, G., & Knösche, T. R. (2020). A novel approach to localize cortical TMS effects. Neuroimage, 209, 116486. doi: [10.1016/j.neuroimage.2019.116486](https://doi.org/10.1016/j.neuroimage.2019.116486)
  - Numssen, O., Zier, A. L., Thielscher, A., Hartwigsen, G., Knösche, T. R., & Weise, K. (2021). Efficient high-resolution TMS mapping of the human motor cortex by nonlinear regression. NeuroImage, 245, 118654. doi:[10.1016/j.neuroimage.2021.118654](https://doi.org/10.1016/j.neuroimage.2021.118654)
  - Weise*, K., Numssen*, O., Kalloch, B., Zier, A. L., Thielscher, A., Hartwigsen°, G., Knösche°, T. R. (2022). Precise transcranial magnetic stimulation motor-mapping. Nature Protocols. doi:[10.1038/s41596-022-00776-6](https://doi.org/10.1038/s41596-022-00776-6) 
