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