pyRiemann:Python软件包，用于协方差矩阵处理和生物信号分类，并在脑计算机接口中得到应用-源码
派里曼
pyriemann是用于通过黎曼几何进行协方差矩阵操纵和分类的python软件包。
主要目标是对多种生物信号进行分类，例如EEG，MEG或EMG。
这项工作仍在进行中，敬请期待。
此代码是BSD许可的（3子句）。
文献资料
该文档位于
安装
使用PyPI
pip install pyriemann
或使用pip + git作为最新版本的代码：
pip install git+https://github.com/alexandrebarachant/pyRiemann
当前不支持Anaconda，如果要使用anaconda，则需要在anaconda中创建虚拟环境，将其激活并使用上述命令进行安装。
从来源
对于最新版本，您可以使用setup.py脚本从源文件中安装软件包。
python setup.py install
或在开发人员模式下能够修改源代码。
python
文件下载
资源详情
[{"title":"（ 71 个子文件 87KB ） pyRiemann:Python软件包，用于协方差矩阵处理和生物信号分类，并在脑计算机接口中得到应用-源码","children":[{"title":"pyRiemann-master","children":[{"title":"setup.py <span style='color:#111;'> 412B </span>","children":null,"spread":false},{"title":".gitignore <span style='color:#111;'> 52B </span>","children":null,"spread":false},{"title":".codeclimate.yml <span style='color:#111;'> 77B </span>","children":null,"spread":false},{"title":"requirements.txt <span style='color:#111;'> 53B </span>","children":null,"spread":false},{"title":".travis.yml <span style='color:#111;'> 736B </span>","children":null,"spread":false},{"title":"MANIFEST.in <span style='color:#111;'> 17B </span>","children":null,"spread":false},{"title":"LICENSE <span style='color:#111;'> 1.48KB </span>","children":null,"spread":false},{"title":"doc","children":[{"title":"_templates","children":[{"title":"function.rst <span style='color:#111;'> 97B </span>","children":null,"spread":false},{"title":"class.rst <span style='color:#111;'> 164B </span>","children":null,"spread":false}],"spread":true},{"title":"whatsnew.rst <span style='color:#111;'> 1.53KB </span>","children":null,"spread":false},{"title":"installing.rst <span style='color:#111;'> 1.06KB </span>","children":null,"spread":false},{"title":"requirements.txt <span style='color:#111;'> 86B </span>","children":null,"spread":false},{"title":"Makefile <span style='color:#111;'> 7.25KB </span>","children":null,"spread":false},{"title":"api.rst <span style='color:#111;'> 3.21KB </span>","children":null,"spread":false},{"title":"index.rst <span style='color:#111;'> 2.96KB </span>","children":null,"spread":false},{"title":"conf.py <span style='color:#111;'> 10.53KB </span>","children":null,"spread":false},{"title":"_static","children":[{"title":"copybutton.js <span style='color:#111;'> 2.59KB </span>","children":null,"spread":false},{"title":"style.css <span style='color:#111;'> 1.95KB </span>","children":null,"spread":false}],"spread":false},{"title":"introduction.rst <span style='color:#111;'> 71B </span>","children":null,"spread":false}],"spread":true},{"title":"setup.cfg <span style='color:#111;'> 370B </span>","children":null,"spread":false},{"title":"pyriemann","children":[{"title":"estimation.py <span style='color:#111;'> 17.18KB </span>","children":null,"spread":false},{"title":"embedding.py <span style='color:#111;'> 3.24KB </span>","children":null,"spread":false},{"title":"classification.py <span style='color:#111;'> 16.85KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 233B </span>","children":null,"spread":false},{"title":"spatialfilters.py <span style='color:#111;'> 15.22KB </span>","children":null,"spread":false},{"title":"clustering.py <span style='color:#111;'> 13.50KB </span>","children":null,"spread":false},{"title":"utils","children":[{"title":"covariance.py <span style='color:#111;'> 4.14KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"base.py <span style='color:#111;'> 2.88KB </span>","children":null,"spread":false},{"title":"mean.py <span style='color:#111;'> 11.29KB </span>","children":null,"spread":false},{"title":"utils.py <span style='color:#111;'> 808B </span>","children":null,"spread":false},{"title":"geodesic.py <span style='color:#111;'> 2.48KB </span>","children":null,"spread":false},{"title":"viz.py <span style='color:#111;'> 1.54KB </span>","children":null,"spread":false},{"title":"ajd.py <span style='color:#111;'> 8.40KB </span>","children":null,"spread":false},{"title":"tangentspace.py <span style='color:#111;'> 2.23KB </span>","children":null,"spread":false},{"title":"distance.py <span style='color:#111;'> 5.36KB </span>","children":null,"spread":false}],"spread":false},{"title":"stats.py <span style='color:#111;'> 14.49KB </span>","children":null,"spread":false},{"title":"channelselection.py <span style='color:#111;'> 5.96KB </span>","children":null,"spread":false},{"title":"tangentspace.py <span style='color:#111;'> 10.72KB </span>","children":null,"spread":false}],"spread":true},{"title":"examples","children":[{"title":"motor-imagery","children":[{"title":"README.txt <span style='color:#111;'> 48B </span>","children":null,"spread":false},{"title":"plot_single.py <span style='color:#111;'> 4.52KB </span>","children":null,"spread":false}],"spread":true},{"title":"SSVEP","children":[{"title":"plot_classify_ssvep_mdm.py <span style='color:#111;'> 9.37KB </span>","children":null,"spread":false},{"title":"README.txt <span style='color:#111;'> 129B </span>","children":null,"spread":false}],"spread":true},{"title":"ERP","children":[{"title":"plot_classify_MEG_mdm.py <span style='color:#111;'> 3.17KB </span>","children":null,"spread":false},{"title":"plot_classify_EEG_tangentspace.py <span style='color:#111;'> 2.73KB </span>","children":null,"spread":false},{"title":"plot_embedding_EEG.py <span style='color:#111;'> 2.60KB </span>","children":null,"spread":false},{"title":"README.txt <span style='color:#111;'> 87B </span>","children":null,"spread":false}],"spread":false},{"title":"README.txt <span style='color:#111;'> 82B </span>","children":null,"spread":false},{"title":"stats","children":[{"title":"plot_oneWay_Manova_frequency.py <span style='color:#111;'> 2.90KB </span>","children":null,"spread":false},{"title":"README.txt <span style='color:#111;'> 67B </span>","children":null,"spread":false},{"title":"oneWay_Manova_ERP.py <span style='color:#111;'> 4.23KB </span>","children":null,"spread":false},{"title":"plot_oneWay_Manova_time.py <span style='color:#111;'> 2.81KB </span>","children":null,"spread":false},{"title":"plot_oneWay_Manova.py <span style='color:#111;'> 4.11KB </span>","children":null,"spread":false}],"spread":false}],"spread":true},{"title":"README.md <span style='color:#111;'> 6.03KB </span>","children":null,"spread":false},{"title":"tests","children":[{"title":"test_classification.py <span style='color:#111;'> 2.85KB </span>","children":null,"spread":false},{"title":"test_embedding.py <span style='color:#111;'> 727B </span>","children":null,"spread":false},{"title":"test_ajd.py <span style='color:#111;'> 928B </span>","children":null,"spread":false},{"title":"test_viz.py <span style='color:#111;'> 912B </span>","children":null,"spread":false},{"title":"test_estimation.py <span style='color:#111;'> 2.61KB </span>","children":null,"spread":false},{"title":"test_utils_distance.py <span style='color:#111;'> 3.66KB </span>","children":null,"spread":false},{"title":"test_utils_geodesic.py <span style='color:#111;'> 2.72KB </span>","children":null,"spread":false},{"title":"test_spatialfilters.py <span style='color:#111;'> 3.86KB </span>","children":null,"spread":false},{"title":"test_utils_tangent_space.py <span style='color:#111;'> 1.05KB </span>","children":null,"spread":false},{"title":"test_utils_base.py <span style='color:#111;'> 1.21KB </span>","children":null,"spread":false},{"title":"test_stats.py <span style='color:#111;'> 1.55KB </span>","children":null,"spread":false},{"title":"test_utils_mean.py <span style='color:#111;'> 3.62KB </span>","children":null,"spread":false},{"title":"test_tangentspace.py <span style='color:#111;'> 2.52KB </span>","children":null,"spread":false},{"title":"test_utils_covariance.py <span style='color:#111;'> 1.83KB </span>","children":null,"spread":false},{"title":"test_channelselection.py <span style='color:#111;'> 913B </span>","children":null,"spread":false},{"title":"test_clustering.py <span style='color:#111;'> 2.13KB </span>","children":null,"spread":false}],"spread":false},{"title":".coveragerc <span style='color:#111;'> 131B </span>","children":null,"spread":false}],"spread":false}],"spread":true}]
评论信息
其他资源
- ArgisCGCS2000（地理+投影坐标系）
- 标准C语言基础教程第四版
- matlab 经典的ICP点云配准算法
- i61850TestSuitev3 破解版
- 我眼中的面向对象方法和结构化方法
- matlab倒车仿真.zip
- java+mysql酒店管理系统
- 液压气动系统设计手册
- ACM国际大学生程序设计竞赛 算法与实现 俞勇.pdf
- IP路由-静态路由.docx
- 上湖-源码
- Coolpad Download Assistant Setup V1.91_150709.exe
- PHMIAS2015.rar
- 基于MSP430的温度采集系统设计
- C#获取本地计算机的硬件信息源代码
- SN8F5708_Sample_Code_V0.3.rar
- STM32F030IAP源码
- U8数据字典
- MPQEditor3.2
- java源码包(包括sun包和具体导入步骤)
- java socket异步长连接demo
- 自定义抽奖系统
- 手动脱WinUpack 壳实战
- Axure RP PRO 原型 模板
免责申明
【只为小站】的资源来自网友分享，仅供学习研究，请务必在下载后24小时内给予删除，不得用于其他任何用途，否则后果自负。基于互联网的特殊性，【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查；无论 【只为小站】 经营者是否已进行审查，用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场，基于网友分享，根据中国法律《信息网络传播权保护条例》第二十二条之规定，若资源存在侵权或相关问题请联系本站客服人员，zhiweidada#qq.com，请把#换成@，本站将给予最大的支持与配合，做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明
本站所有资源不代表本站的观点或立场，基于网友分享，根据中国法律《信息网络传播权保护条例》第二十二条之规定，若资源存在侵权或相关问题请联系本站客服人员，zhiweidada#qq.com，请把#换成@，本站将给予最大的支持与配合，做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明