将EEG独立成分分析分类工具ICLabel移植到Python，实现跨平台兼容。
Automatic EEG Independent Component Classification Using ICLabel in Python
- 基于标准EEGLAB数据结构重构ICLabel的Python版本
- 14名受试者数据对比显示分类结果差异小于0.001%
- 适合需要在Python环境中处理EEG数据的研究者
ICLabel是EEGLAB中用于自动处理脑电图（EEG）数据的关键插件，通过独立成分分析（ICA）分解数据后对得到的独立成分（ICs）进行分类。尽管EEGLAB支持基于开源Octave的高性能计算平台，但其专用神经网络架构导致ICLabel无法在Octave上运行。为提升跨平台兼容性，我们开发了使用标准EEGLAB数据结构的Python版ICLabel。通过对14名受试者的数据进行比较，发现Python与MATLAB版本的分类结果几乎完全一致，各成分在7类中的分类概率差异低于0.001%。
原文摘要 · Abstract (English)
ICLabel is an important plug-in function in EEGLAB, the most widely used software for EEG data processing. A powerful approach to automated processing of EEG data involves decomposing the data by Independent Component Analysis (ICA) and then classifying the resulting independent components (ICs) using ICLabel. While EEGLAB pipelines support high-performance computing (HPC) platforms running the open-source Octave interpreter, the ICLabel plug-in is incompatible with Octave because of its specialized neural network architecture. To enhance cross-platform compatibility, we developed a Python version of ICLabel that uses standard EEGLAB data structures. We compared ICLabel MATLAB and Python implementations to data from 14 subjects. ICLabel returns the likelihood of classification in 7 classes of components for each ICA component. The returned IC classifications were virtually identical between Python and MATLAB, with differences in classification percentage below 0.001%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。