BSD 3-Clause "New" or "Revised" License

项目介绍

cbrnr/mnelab 是一个托管在 GitHub 上的开源项目,主要使用 Python 编写,采用 BSD 3-Clause "New" or "Revised" License 许可证,目前获得 291 个 Star、78 次 Fork。

仓库全名
cbrnr/mnelab
开源许可证
BSD 3-Clause "New" or "Revised" License
Star 数
291
Fork 数
78
未关闭 Issue
23

README 摘要

MNELAB MNELAB is a graphical user interface for MNE-Python (the most popular Python package for EEG/MEG analysis). Key features include: Cross-platform support (Linux, macOS, Windows). A command history that records the underlying MNE-Python commands for each action, allowing users to learn how to use MNE-Python and to reproduce their analyses in code. Import data from various formats supported by MNE-Python, and…

完整内容见 GitHub 原始 README 或本页 。

常见问题

mnelab 用什么语言开发?

主要使用 Python 编写,采用 BSD 3-Clause "New" or "Revised" License 许可证,目前有 291 个 Star。

mnelab 可以免费商用吗?

仓库采用 BSD 3-Clause "New" or "Revised" License 许可证,可免费使用与修改,商业使用请遵守许可证条款(例如保留版权声明与许可声明)。

如何获取 mnelab 的源码?

可以直接克隆仓库:git clone https://github.com/cbrnr/mnelab.git;安装与运行步骤见仓库 README。

mnelab 最近还在维护吗?

仓库最近一次更新为 5月29日,当前有 23 个未关闭 Issue;Star 数为 291,可作为社区活跃度的参考。

关于

GitHub 原始描述(英文)

MNELAB – a GUI for MNE-Python

描述取自 GitHub 官方 API,保持仓库作者书写的原文。

简要分析

热度分
738
价值分
225
活跃状态
活跃

语言占比

Inno Setup
PowerShell
Python
Shell

Release

README

MNELAB

MNELAB is a graphical user interface for MNE-Python (the most popular Python package for EEG/MEG analysis).

Key features include:

  • Cross-platform support (Linux, macOS, Windows).
  • A command history that records the underlying MNE-Python commands for each action, allowing users to learn how to use MNE-Python and to reproduce their analyses in code.
  • Import data from various formats supported by MNE-Python, and some additional formats like XDF, MATLAB, NumPy, and BVRF.
  • Export to EDF, BDF, BrainVision, EEGLAB, and FIFF formats.
  • XDF-specific features such as chunk inspection (useful for debugging corrupted XDF files), stream selection, metadata inspection, resampling, gap detection and filling, and more.
  • Support for various ICA algorithms, including FastICA, Infomax ICA, and PICARD.
  • Automatic classification of independent components using ICLabel.
  • Comprehensive tools for managing events and annotations.
  • Support for channel locations (montages), re-referencing, cropping, filtering, epoching, and more.
  • Plotting functions for raw data, epochs, evoked responses, independent components, ERD/ERS maps, and more.

Documentation

The documentation contains hands-on examples and tutorials for different use cases. Check out the changelog to learn what we added, changed, or fixed.

Quick Start

We recommend using the standalone installers for macOS and Windows:

If you use Arch Linux, you can install MNELAB from the AUR (e.g., yay -S python-mnelab).

Alternatively, you can use uv to run MNELAB directly without installing it (this works on all platforms, but for the best experience we recommend using the standalone installers when available):

uvx mnelab

Advanced Usage

To run the latest development version:

uvx --from https://github.com/cbrnr/mnelab/archive/refs/heads/main.zip mnelab

On Linux, running MNELAB via uvx mnelab uses the Fusion style shipped with PySide6, which may not fit well with the rest of the system. However, if you use KDE, you can set the QT_PLUGIN_PATH environment variable to force the use of the native KDE theme instead, for example:

QT_PLUGIN_PATH=/usr/lib/qt6/plugins uvx mnelab

Contributing

The contributing guide provides detailed instructions for contributing to MNELAB.

Citing

If you use MNELAB in your research, please cite the following paper (see also CITATION.cff):

Brunner, C. (2022). MNELAB: a graphical user interface for MNE-Python. Journal of Open Source Software, 7(78), 4650, doi:10.21105/joss.04650.

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