# Braindecode 代码仓库与文档

> AI 辅助翻译，非官方版本。专业判断、引用和争议应以原文及其勘误为准。

- 来源 ID：S0146
- 原始来源：https://raw.githubusercontent.com/braindecode/braindecode/66f3666342a5c72e4c1f88181318afb6b27601a1/README.rst
- 许可：BSD-3-Clause
- 翻译方法：Independent manual review; SHA-256-pinned source and translation
- 审校状态：独立人工审校完成

## 原始文件映射

- [README.rst](../original/README.rst)

## 许可文件（保留原文）

- [github-license-api.json](../license/github-license-api.json)：法律或许可证据原文未翻译
- [LICENSE.txt](../license/LICENSE.txt)：法律或许可证据原文未翻译
- [NOTICE.txt](../license/NOTICE.txt)：法律或许可证据原文未翻译

## 原始文件：README.rst

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.17699192.svg
    :target: https://doi.org/10.5281/zenodo.17699192
    :alt: DOI

.. image:: https://github.com/braindecode/braindecode/workflows/docs/badge.svg
    :target: https://github.com/braindecode/braindecode/actions/workflows/docs.yml
    :alt: 文档构建状态

.. image:: https://github.com/braindecode/braindecode/workflows/tests/badge.svg
    :target: https://github.com/braindecode/braindecode/actions/workflows/tests.yml
    :alt: 测试构建状态

.. image:: https://codecov.io/gh/braindecode/braindecode/branch/master/graph/badge.svg
    :target: https://codecov.io/gh/braindecode/braindecode
    :alt: 代码覆盖率

.. image:: https://img.shields.io/pypi/v/braindecode?color=blue&style=flat-square
    :target: https://pypi.org/project/braindecode/
    :alt: PyPI

.. image:: https://img.shields.io/pypi/v/braindecode?label=version&color=orange&style=flat-square
    :target: https://pypi.org/project/braindecode/
    :alt: 版本

.. image:: https://img.shields.io/pypi/pyversions/braindecode?style=flat-square
    :target: https://pypi.org/project/braindecode/
    :alt: Python 版本

.. image:: https://pepy.tech/badge/braindecode
    :target: https://pepy.tech/project/braindecode
    :alt: 下载量

.. |Braindecode| image:: https://user-images.githubusercontent.com/42702466/177958779-b00628aa-9155-4c51-96d1-d8c345aff575.svg

.. _braindecode: braindecode.org/

#############
 Braindecode
#############

Braindecode 是一个开源 Python 工具箱，用深度学习模型解码原始电生理脑数据。它包含数据集
获取器、数据预处理和可视化工具，以及多种用于分析 EEG、ECoG 和 MEG 的深度学习架构与
数据增强方法实现。

它既面向希望使用深度学习的神经科学家，也面向希望研究神经生理数据的深度学习研究人员。

##########################
 安装 Braindecode
##########################

1. 从 http://pytorch.org/ 安装 pytorch（无需安装 torchvision）。
2. 如果希望从 `MOABB <https://github.com/NeuroTechX/moabb>`_ 下载 EEG 数据集，请安装 MOABB：

.. code-block:: bash

    pip install moabb

3. 通过 pip 安装 braindecode 的最新发行版：

.. code-block:: bash

    pip install braindecode

如需安装 braindecode 的最新开发版本，请参阅
`贡献页面 <https://github.com/braindecode/braindecode/blob/master/CONTRIBUTING.md>`__。

###############
 文档
###############

文档位于 https://braindecode.org，同时提供稳定版和开发版文档。

#############################
 为 Braindecode 贡献
#############################

在 braindecode GitHub 仓库中可以找到贡献指南：

https://github.com/braindecode/braindecode/blob/master/CONTRIBUTING.md

########
 引用
########

如果在科研工作中使用 Braindecode，请使用下方徽章所示的全局 Zenodo DOI 引用本软件：

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.17699192.svg
    :target: https://doi.org/10.5281/zenodo.17699192
    :alt: DOI

可以使用以下 BibTeX 条目：

.. code-block:: bibtex

    @software{braindecode,
      author = {Aristimunha, Bruno and
                Guetschel, Pierre and
                Wimpff, Martin and
                Gemein, Lukas and
                Rommel, Cedric and
                Banville, Hubert and
                Sliwowski, Maciej and
                Wilson, Daniel and
                Brandt, Simon and
                Gnassounou, Théo and
                Paillard, Joseph and
                {Junqueira Lopes}, Bruna and
                Sedlar, Sara and
                Moreau, Thomas and
                Chevallier, Sylvain and
                Gramfort, Alexandre and
                Schirrmeister, Robin Tibor},
      title = {Braindecode: toolbox for decoding raw electrophysiological brain data
               with deep learning models},
      url = {https://github.com/braindecode/braindecode},
      doi = {10.5281/zenodo.17699192},
      publisher = {Zenodo},
      license = {BSD-3-Clause},
    }

此外，强烈建议引用最初介绍 Braindecode 库的文章。该文已成为许多 EEG 深度学习研究的基础
参考资料，请使用以下条目：

.. 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},
    }

还请引用 braindecode 所使用的 `MNE-Python <https://mne.tools>`_ 软件：

.. code-block:: bibtex

    @article{10.3389/fnins.2013.00267,
    author={Gramfort, Alexandre and Luessi, Martin and Larson, Eric and Engemann, Denis and Strohmeier, Daniel and Brodbeck, Christian and Goj, Roman and Jas, Mainak and Brooks, Teon and Parkkonen, Lauri and Hämäläinen, Matti},
    title={{MEG and EEG data analysis with MNE-Python}},
    journal={Frontiers in Neuroscience},
    volume={7},
    pages={267},
    year={2013},
    url={https://www.frontiersin.org/article/10.3389/fnins.2013.00267},
    doi={10.3389/fnins.2013.00267},
    issn={1662-453X},
    }

***********
 许可
***********

This project is primarily licensed under the BSD-3-Clause License.

附加组件
=====================

Some components within this repository are licensed under the Creative Commons
Attribution-NonCommercial 4.0 International License.

更多详细信息请参阅 ``LICENSE`` 和 ``NOTICE`` 文件。

## 原文 DOI 与数字核对索引

下列不变量从原始载荷自动提取，仅用于核对译文完整性，不构成额外证据摘要。

- `10.1002`
- `10.1002/hbm.23730`
- `10.3389`
- `10.3389/fnins.2013.00267`
- `10.5281`
- `10.5281/zenodo.17699192`
- `10.5281/zenodo.17699192.svg`
- `0193`
- `062`
- `1`
- `1097`
- `1662`
- `17699192`
- `177958779`
- `2`
- `2013`
- `2013.00267`
- `2017`
- `23730`
- `267`
- `3`
- `3730`
- `4`
- `4.0`
- `42702466`
- `45`
- `7`
- `75`
- `9`
- `9155`