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pyRiemann: Biosignals classification with Riemannian geometry
=============================================================

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pyRiemann is a Python machine learning package based on scikit-learn API.
It provides a high-level interface for processing and classification of real (resp. complex)-valued multivariate data
through the Riemannian geometry of symmetric (resp. Hermitian) positive definite (SPD) (resp. HPD) matrices.

pyRiemann aims at being a generic package for multivariate data analysis
but has been designed around biosignals (like EEG, MEG or EMG) manipulation applied to brain-computer interface (BCI),
estimating covariance matrices from multichannel time series, and classifying them using the Riemannian geometry of SPD matrices.

For a brief introduction to the ideas behind the package, you can read the
:ref:`introductory notes <introduction>`. More practical information is on the
:ref:`installation page <installing>`. You may also want to browse the
`example gallery <auto_examples/index.html>`_ to get a sense for what you can do with pyRiemann
and :ref:`API reference <api_ref>` to find out how.

To see the code or report a bug, please visit the `github repository
<https://github.com/pyRiemann/pyRiemann>`_.

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.. toctree::
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   Introduction <introduction>
   Release notes <whatsnew>
   Installing <installing>
   Example gallery <auto_examples/index>
   API reference <api>

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