%global _empty_manifest_terminate_build 0
Name: python-pyriemann
Version: 0.4
Release: 1
Summary: Biosignals classification with Riemannian geometry
License: BSD (3-clause)
URL: https://pyriemann.readthedocs.io
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ee/5e/ac8347d876bb0c04498196b33fbf25f2a21eace9fe8f791292598773508a/pyriemann-0.4.tar.gz
BuildArch: noarch
%description
# pyRiemann
[](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue)
[](https://badge.fury.io/py/pyriemann)
[](https://github.com/pyRiemann/pyRiemann/actions)
[](https://codecov.io/gh/pyRiemann/pyRiemann)
[](http://pyriemann.readthedocs.io/en/latest/?badge=latest)
[](https://doi.org/10.5281/zenodo.593816)
[](https://pepy.tech/project/pyriemann)
pyRiemann is a Python machine learning package based on [scikit-learn](http://scikit-learn.org/stable/modules/classes.html) API.
It provides a high-level interface for processing and classification of multivariate time series
through the Riemannian geometry of symmetric positive definite (SPD) matrices.
pyRiemann aims at being a generic package for multivariate time series classification
but has been designed around multichannel biosignals (like EEG, MEG or EMG) manipulation applied to brain-computer interface (BCI),
transforming multichannel time series into covariance matrices, and classifying them using the Riemannian geometry of SPD matrices [[1]](#1).
For BCI applications, studied paradigms are motor imagery [[2]](#2) [[3]](#3), event-related potentials (ERP) [[4]](#4) and steady-state visually evoked potentials (SSVEP) [[5]](#5).
Using extended labels, API allows transfer learning between sessions or subjects [[6]](#6).
This code is BSD-licenced (3 clause).
## Documentation
The documentation is available on http://pyriemann.readthedocs.io/en/latest/
## Install
#### Using PyPI
```
pip install pyriemann
```
or using pip+git for the latest version of the code :
```
pip install git+https://github.com/pyRiemann/pyRiemann
```
Anaconda is not currently supported, if you want to use anaconda, you need to create a virtual environment in anaconda,
activate it and use the above command to install it.
#### From sources
For the latest version, you can install the package from the sources using the setup.py script
```
python setup.py install
```
or in developer mode to be able to modify the sources.
```
python setup.py develop
```
## How to use it
Most of the functions mimic the scikit-learn API, and therefore can be directly used with sklearn.
For example, for cross-validation classification of EEG signal using the MDM algorithm described in [[2]](#2), it is easy as:
```python
import pyriemann
from sklearn.model_selection import cross_val_score
# load your data
X = ... # EEG data, in format n_epochs x n_channels x n_times
y = ... # labels
# estimate covariance matrices
cov = pyriemann.estimation.Covariances().fit_transform(X)
# cross validation
mdm = pyriemann.classification.MDM()
accuracy = cross_val_score(mdm, cov, y)
print(accuracy.mean())
```
You can also pipeline methods using sklearn pipeline framework.
For example, to classify EEG signal using a SVM classifier in the tangent space, described in [[3]](#3):
```python
from pyriemann.estimation import Covariances
from pyriemann.tangentspace import TangentSpace
from sklearn.pipeline import make_pipeline
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score
# load your data
X = ... # EEG data, in format n_epochs x n_channels x n_times
y = ... # labels
# build your pipeline
covest = Covariances()
ts = TangentSpace()
svc = SVC(kernel='linear')
clf = make_pipeline(covest, ts, svc)
# cross validation
accuracy = cross_val_score(clf, X, y)
print(accuracy.mean())
```
**Check out the example folder for more examples !**
# Contribution Guidelines
The package aims at adopting the [scikit-learn](http://scikit-learn.org/stable/developers/contributing.html#contributing-code)
and [MNE-Python](https://mne.tools/stable/install/contributing.html) conventions as much as possible.
See their contribution guidelines before contributing to the repository.
# Testing
If you make a modification, run the test suite before submitting a pull request
```
pytest
```
# How to cite
```bibtex
@software{pyriemann,
author = {Alexandre Barachant and
Quentin Barthélemy and
Jean-Rémi King and
Alexandre Gramfort and
Sylvain Chevallier and
Pedro L. C. Rodrigues and
Emanuele Olivetti and
Vladislav Goncharenko and
Gabriel Wagner vom Berg and
Ghiles Reguig and
Arthur Lebeurrier and
Erik Bjäreholt and
Maria Sayu Yamamoto and
Pierre Clisson and
Marie-Constance Corsi},
title = {pyRiemann/pyRiemann: v0.3},
month = jul,
year = 2022,
publisher = {Zenodo},
version = {v0.3},
doi = {10.5281/zenodo.7547583},
url = {https://doi.org/10.5281/zenodo.7547583}
}
```
# References
[1]
M. Congedo, A. Barachant and R. Bhatia, "Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review".
Brain-Computer Interfaces, 4.3, pp. 155-174, 2017. [link](https://hal.science/hal-01570120/document)
[2]
A. Barachant, S. Bonnet, M. Congedo and C. Jutten, "Multiclass Brain-Computer Interface Classification by Riemannian Geometry".
IEEE Transactions on Biomedical Engineering, vol. 59, no. 4, pp. 920-928, 2012. [link](https://hal.archives-ouvertes.fr/hal-00681328)
[3]
A. Barachant, S. Bonnet, M. Congedo and C. Jutten, "Classification of covariance matrices using a Riemannian-based kernel for BCI applications".
Neurocomputing, 112, pp. 172-178, 2013. [link](https://hal.archives-ouvertes.fr/hal-00820475/)
[4]
A. Barachant and M. Congedo, "A Plug&Play P300 BCI Using Information Geometry".
Research report, 2014. [link](http://arxiv.org/abs/1409.0107)
[5]
EK. Kalunga, S. Chevallier, Q. Barthélemy, K. Djouani, E. Monacelli and Y. Hamam, "Online SSVEP-based BCI using Riemannian geometry".
Neurocomputing, 191, pp. 55-68, 2014. [link](https://hal.science/hal-01351623/file/Kalunga-Chevallier-Barthelemy-Online%20SSVEP-based%20BCI%20using%20Riemannian%20Geometry-Neurocomputing-16.pdf)
[6]
PLC. Rodrigues, C. Jutten and M. Congedo, "Riemannian Procrustes analysis: transfer learning for brain-computer interfaces".
IEEE Transactions on Biomedical Engineering, vol. 66, no. 8, pp. 2390-2401, 2018. [link](https://hal.archives-ouvertes.fr/hal-01971856)
%package -n python3-pyriemann
Summary: Biosignals classification with Riemannian geometry
Provides: python-pyriemann
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-pyriemann
# pyRiemann
[](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue)
[](https://badge.fury.io/py/pyriemann)
[](https://github.com/pyRiemann/pyRiemann/actions)
[](https://codecov.io/gh/pyRiemann/pyRiemann)
[](http://pyriemann.readthedocs.io/en/latest/?badge=latest)
[](https://doi.org/10.5281/zenodo.593816)
[](https://pepy.tech/project/pyriemann)
pyRiemann is a Python machine learning package based on [scikit-learn](http://scikit-learn.org/stable/modules/classes.html) API.
It provides a high-level interface for processing and classification of multivariate time series
through the Riemannian geometry of symmetric positive definite (SPD) matrices.
pyRiemann aims at being a generic package for multivariate time series classification
but has been designed around multichannel biosignals (like EEG, MEG or EMG) manipulation applied to brain-computer interface (BCI),
transforming multichannel time series into covariance matrices, and classifying them using the Riemannian geometry of SPD matrices [[1]](#1).
For BCI applications, studied paradigms are motor imagery [[2]](#2) [[3]](#3), event-related potentials (ERP) [[4]](#4) and steady-state visually evoked potentials (SSVEP) [[5]](#5).
Using extended labels, API allows transfer learning between sessions or subjects [[6]](#6).
This code is BSD-licenced (3 clause).
## Documentation
The documentation is available on http://pyriemann.readthedocs.io/en/latest/
## Install
#### Using PyPI
```
pip install pyriemann
```
or using pip+git for the latest version of the code :
```
pip install git+https://github.com/pyRiemann/pyRiemann
```
Anaconda is not currently supported, if you want to use anaconda, you need to create a virtual environment in anaconda,
activate it and use the above command to install it.
#### From sources
For the latest version, you can install the package from the sources using the setup.py script
```
python setup.py install
```
or in developer mode to be able to modify the sources.
```
python setup.py develop
```
## How to use it
Most of the functions mimic the scikit-learn API, and therefore can be directly used with sklearn.
For example, for cross-validation classification of EEG signal using the MDM algorithm described in [[2]](#2), it is easy as:
```python
import pyriemann
from sklearn.model_selection import cross_val_score
# load your data
X = ... # EEG data, in format n_epochs x n_channels x n_times
y = ... # labels
# estimate covariance matrices
cov = pyriemann.estimation.Covariances().fit_transform(X)
# cross validation
mdm = pyriemann.classification.MDM()
accuracy = cross_val_score(mdm, cov, y)
print(accuracy.mean())
```
You can also pipeline methods using sklearn pipeline framework.
For example, to classify EEG signal using a SVM classifier in the tangent space, described in [[3]](#3):
```python
from pyriemann.estimation import Covariances
from pyriemann.tangentspace import TangentSpace
from sklearn.pipeline import make_pipeline
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score
# load your data
X = ... # EEG data, in format n_epochs x n_channels x n_times
y = ... # labels
# build your pipeline
covest = Covariances()
ts = TangentSpace()
svc = SVC(kernel='linear')
clf = make_pipeline(covest, ts, svc)
# cross validation
accuracy = cross_val_score(clf, X, y)
print(accuracy.mean())
```
**Check out the example folder for more examples !**
# Contribution Guidelines
The package aims at adopting the [scikit-learn](http://scikit-learn.org/stable/developers/contributing.html#contributing-code)
and [MNE-Python](https://mne.tools/stable/install/contributing.html) conventions as much as possible.
See their contribution guidelines before contributing to the repository.
# Testing
If you make a modification, run the test suite before submitting a pull request
```
pytest
```
# How to cite
```bibtex
@software{pyriemann,
author = {Alexandre Barachant and
Quentin Barthélemy and
Jean-Rémi King and
Alexandre Gramfort and
Sylvain Chevallier and
Pedro L. C. Rodrigues and
Emanuele Olivetti and
Vladislav Goncharenko and
Gabriel Wagner vom Berg and
Ghiles Reguig and
Arthur Lebeurrier and
Erik Bjäreholt and
Maria Sayu Yamamoto and
Pierre Clisson and
Marie-Constance Corsi},
title = {pyRiemann/pyRiemann: v0.3},
month = jul,
year = 2022,
publisher = {Zenodo},
version = {v0.3},
doi = {10.5281/zenodo.7547583},
url = {https://doi.org/10.5281/zenodo.7547583}
}
```
# References
[1]
M. Congedo, A. Barachant and R. Bhatia, "Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review".
Brain-Computer Interfaces, 4.3, pp. 155-174, 2017. [link](https://hal.science/hal-01570120/document)
[2]
A. Barachant, S. Bonnet, M. Congedo and C. Jutten, "Multiclass Brain-Computer Interface Classification by Riemannian Geometry".
IEEE Transactions on Biomedical Engineering, vol. 59, no. 4, pp. 920-928, 2012. [link](https://hal.archives-ouvertes.fr/hal-00681328)
[3]
A. Barachant, S. Bonnet, M. Congedo and C. Jutten, "Classification of covariance matrices using a Riemannian-based kernel for BCI applications".
Neurocomputing, 112, pp. 172-178, 2013. [link](https://hal.archives-ouvertes.fr/hal-00820475/)
[4]
A. Barachant and M. Congedo, "A Plug&Play P300 BCI Using Information Geometry".
Research report, 2014. [link](http://arxiv.org/abs/1409.0107)
[5]
EK. Kalunga, S. Chevallier, Q. Barthélemy, K. Djouani, E. Monacelli and Y. Hamam, "Online SSVEP-based BCI using Riemannian geometry".
Neurocomputing, 191, pp. 55-68, 2014. [link](https://hal.science/hal-01351623/file/Kalunga-Chevallier-Barthelemy-Online%20SSVEP-based%20BCI%20using%20Riemannian%20Geometry-Neurocomputing-16.pdf)
[6]
PLC. Rodrigues, C. Jutten and M. Congedo, "Riemannian Procrustes analysis: transfer learning for brain-computer interfaces".
IEEE Transactions on Biomedical Engineering, vol. 66, no. 8, pp. 2390-2401, 2018. [link](https://hal.archives-ouvertes.fr/hal-01971856)
%package help
Summary: Development documents and examples for pyriemann
Provides: python3-pyriemann-doc
%description help
# pyRiemann
[](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue)
[](https://badge.fury.io/py/pyriemann)
[](https://github.com/pyRiemann/pyRiemann/actions)
[](https://codecov.io/gh/pyRiemann/pyRiemann)
[](http://pyriemann.readthedocs.io/en/latest/?badge=latest)
[](https://doi.org/10.5281/zenodo.593816)
[](https://pepy.tech/project/pyriemann)
pyRiemann is a Python machine learning package based on [scikit-learn](http://scikit-learn.org/stable/modules/classes.html) API.
It provides a high-level interface for processing and classification of multivariate time series
through the Riemannian geometry of symmetric positive definite (SPD) matrices.
pyRiemann aims at being a generic package for multivariate time series classification
but has been designed around multichannel biosignals (like EEG, MEG or EMG) manipulation applied to brain-computer interface (BCI),
transforming multichannel time series into covariance matrices, and classifying them using the Riemannian geometry of SPD matrices [[1]](#1).
For BCI applications, studied paradigms are motor imagery [[2]](#2) [[3]](#3), event-related potentials (ERP) [[4]](#4) and steady-state visually evoked potentials (SSVEP) [[5]](#5).
Using extended labels, API allows transfer learning between sessions or subjects [[6]](#6).
This code is BSD-licenced (3 clause).
## Documentation
The documentation is available on http://pyriemann.readthedocs.io/en/latest/
## Install
#### Using PyPI
```
pip install pyriemann
```
or using pip+git for the latest version of the code :
```
pip install git+https://github.com/pyRiemann/pyRiemann
```
Anaconda is not currently supported, if you want to use anaconda, you need to create a virtual environment in anaconda,
activate it and use the above command to install it.
#### From sources
For the latest version, you can install the package from the sources using the setup.py script
```
python setup.py install
```
or in developer mode to be able to modify the sources.
```
python setup.py develop
```
## How to use it
Most of the functions mimic the scikit-learn API, and therefore can be directly used with sklearn.
For example, for cross-validation classification of EEG signal using the MDM algorithm described in [[2]](#2), it is easy as:
```python
import pyriemann
from sklearn.model_selection import cross_val_score
# load your data
X = ... # EEG data, in format n_epochs x n_channels x n_times
y = ... # labels
# estimate covariance matrices
cov = pyriemann.estimation.Covariances().fit_transform(X)
# cross validation
mdm = pyriemann.classification.MDM()
accuracy = cross_val_score(mdm, cov, y)
print(accuracy.mean())
```
You can also pipeline methods using sklearn pipeline framework.
For example, to classify EEG signal using a SVM classifier in the tangent space, described in [[3]](#3):
```python
from pyriemann.estimation import Covariances
from pyriemann.tangentspace import TangentSpace
from sklearn.pipeline import make_pipeline
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score
# load your data
X = ... # EEG data, in format n_epochs x n_channels x n_times
y = ... # labels
# build your pipeline
covest = Covariances()
ts = TangentSpace()
svc = SVC(kernel='linear')
clf = make_pipeline(covest, ts, svc)
# cross validation
accuracy = cross_val_score(clf, X, y)
print(accuracy.mean())
```
**Check out the example folder for more examples !**
# Contribution Guidelines
The package aims at adopting the [scikit-learn](http://scikit-learn.org/stable/developers/contributing.html#contributing-code)
and [MNE-Python](https://mne.tools/stable/install/contributing.html) conventions as much as possible.
See their contribution guidelines before contributing to the repository.
# Testing
If you make a modification, run the test suite before submitting a pull request
```
pytest
```
# How to cite
```bibtex
@software{pyriemann,
author = {Alexandre Barachant and
Quentin Barthélemy and
Jean-Rémi King and
Alexandre Gramfort and
Sylvain Chevallier and
Pedro L. C. Rodrigues and
Emanuele Olivetti and
Vladislav Goncharenko and
Gabriel Wagner vom Berg and
Ghiles Reguig and
Arthur Lebeurrier and
Erik Bjäreholt and
Maria Sayu Yamamoto and
Pierre Clisson and
Marie-Constance Corsi},
title = {pyRiemann/pyRiemann: v0.3},
month = jul,
year = 2022,
publisher = {Zenodo},
version = {v0.3},
doi = {10.5281/zenodo.7547583},
url = {https://doi.org/10.5281/zenodo.7547583}
}
```
# References
[1]
M. Congedo, A. Barachant and R. Bhatia, "Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review".
Brain-Computer Interfaces, 4.3, pp. 155-174, 2017. [link](https://hal.science/hal-01570120/document)
[2]
A. Barachant, S. Bonnet, M. Congedo and C. Jutten, "Multiclass Brain-Computer Interface Classification by Riemannian Geometry".
IEEE Transactions on Biomedical Engineering, vol. 59, no. 4, pp. 920-928, 2012. [link](https://hal.archives-ouvertes.fr/hal-00681328)
[3]
A. Barachant, S. Bonnet, M. Congedo and C. Jutten, "Classification of covariance matrices using a Riemannian-based kernel for BCI applications".
Neurocomputing, 112, pp. 172-178, 2013. [link](https://hal.archives-ouvertes.fr/hal-00820475/)
[4]
A. Barachant and M. Congedo, "A Plug&Play P300 BCI Using Information Geometry".
Research report, 2014. [link](http://arxiv.org/abs/1409.0107)
[5]
EK. Kalunga, S. Chevallier, Q. Barthélemy, K. Djouani, E. Monacelli and Y. Hamam, "Online SSVEP-based BCI using Riemannian geometry".
Neurocomputing, 191, pp. 55-68, 2014. [link](https://hal.science/hal-01351623/file/Kalunga-Chevallier-Barthelemy-Online%20SSVEP-based%20BCI%20using%20Riemannian%20Geometry-Neurocomputing-16.pdf)
[6]
PLC. Rodrigues, C. Jutten and M. Congedo, "Riemannian Procrustes analysis: transfer learning for brain-computer interfaces".
IEEE Transactions on Biomedical Engineering, vol. 66, no. 8, pp. 2390-2401, 2018. [link](https://hal.archives-ouvertes.fr/hal-01971856)
%prep
%autosetup -n pyriemann-0.4
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-pyriemann -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri Apr 21 2023 Python_Bot - 0.4-1
- Package Spec generated