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braindecode/docs/index.rst
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2017-11-03 17:47:03 +01:00

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Welcome to Braindecode
======================
A deep learning toolbox to decode raw time-domain EEG.
For EEG researchers that want to work with deep learning and
deep learning researchers that want to work with EEG data.
For now focussed on convolutional networks.
Installation
============
1. Install pytorch from http://pytorch.org/ (you don't need to install torchvision).
2. Install numpy (necessary for resamply installation to work), e.g.:
.. code-block:: bash
pip install numpy
3. Install braindecode via pip:
.. code-block:: bash
pip install braindecode
Tutorials
=========
.. toctree::
:maxdepth: 1
notebooks/TrialWise_Decoding.ipynb
notebooks/Cropped_Decoding.ipynb
notebooks/Experiment_Class.ipynb
notebooks/visualization/Perturbation.ipynb
Troubleshooting
===============
Please report any issues on github: https://github.com/robintibor/braindecode
API
===
.. autosummary::
:toctree: source
braindecode.datautil
braindecode.experiments
braindecode.mne_ext
braindecode.models
braindecode.torch_ext
braindecode.visualization
Citing
======
If you use this code in a scientific publication, please cite us as:
.. code-block:: bibtex
@article {HBM:HBM23730,
author = {Schirrmeister, Robin Tibor and Springenberg, Jost Tobias and Fiederer,
Lukas Dominique Josef and Glasstetter, Martin and Eggensperger, Katharina and Tangermann, Michael and
Hutter, Frank and Burgard, Wolfram and Ball, Tonio},
title = {Deep learning with convolutional neural networks for EEG decoding and visualization},
journal = {Human Brain Mapping},
issn = {1097-0193},
url = {http://dx.doi.org/10.1002/hbm.23730},
doi = {10.1002/hbm.23730},
month = {aug},
year = {2017},
keywords = {electroencephalography, EEG analysis, machine learning, end-to-end learning, brain–machine interface,
brain–computer interface, model interpretability, brain mapping},
}
Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`
.. _GitHub: https://github.com/robintibor/braindecode