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结合独立成分分析和主成分分析的半自动脑电图预处理方案。

Protocol for semi-automatic EEG preprocessing incorporating independent component analysis and principal component analysis.

作者信息

Ouyang Guang, Li Yingzhe

机构信息

Complex Neural Signals Decoding Lab, Faculty of Education, The University of Hong Kong, Hong Kong, China.

Complex Neural Signals Decoding Lab, Faculty of Education, The University of Hong Kong, Hong Kong, China.

出版信息

STAR Protoc. 2025 Mar 21;6(1):103682. doi: 10.1016/j.xpro.2025.103682. Epub 2025 Mar 6.

DOI:10.1016/j.xpro.2025.103682
PMID:40053447
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11930125/
Abstract

Preprocessing is a critical yet challenging step in electroencephalography (EEG) research due to its significant potential impact on results. We present a protocol for semi-automatic EEG preprocessing incorporating independent component analysis (ICA) and principal component analysis (PCA) with step-by-step quality checking to ensure removal of large-amplitude artifacts. We describe steps for interpolating bad channels, removal of major artifacts by ICA and PCA correction, and exporting processed data. This protocol produced consistent results from users with a broad range of experience.

摘要

预处理是脑电图（EEG）研究中一个关键但具有挑战性的步骤，因为它对结果有重大潜在影响。我们提出了一种半自动EEG预处理方案，该方案结合了独立成分分析（ICA）和主成分分析（PCA），并进行逐步质量检查，以确保去除大幅度伪迹。我们描述了插补坏通道、通过ICA和PCA校正去除主要伪迹以及导出处理后数据的步骤。该方案在具有广泛经验的用户中产生了一致的结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/820f0af13ddb/gr16.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/e4b5ddb63502/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/5033046ff986/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/84a67012107a/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/2e9610098c3f/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/c6b2d1d6db43/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/1b93f17ac939/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/1a54e3dc82db/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/7964803d78d9/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/a81b7874cbaa/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/f5b297b65300/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/fc846313a781/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/91ddbfed95d0/gr11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/b41193f1173b/gr12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/20cd57db85db/gr13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/18a38fd733c2/gr14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/d756c9851d3d/gr15.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/820f0af13ddb/gr16.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/e4b5ddb63502/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/5033046ff986/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/84a67012107a/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/2e9610098c3f/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/c6b2d1d6db43/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/1b93f17ac939/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/1a54e3dc82db/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/7964803d78d9/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/a81b7874cbaa/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/f5b297b65300/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/fc846313a781/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/91ddbfed95d0/gr11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/b41193f1173b/gr12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/20cd57db85db/gr13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/18a38fd733c2/gr14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/d756c9851d3d/gr15.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb3/11930125/820f0af13ddb/gr16.jpg

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本文引用的文献

1
Beyond single paradigms, pipelines, and outcomes: Embracing multiverse analyses in psychophysiology.超越单一范式、管道和结果：在心理生理学中采用多元宇宙分析。
Int J Psychophysiol. 2024 Mar;197:112311. doi: 10.1016/j.ijpsycho.2024.112311. Epub 2024 Jan 29.
2
Introducing RELAX: An automated pre-processing pipeline for cleaning EEG data - Part 1: Algorithm and application to oscillations.介绍RELAX：一种用于清理脑电图（EEG）数据的自动化预处理管道——第1部分：算法及其在振荡中的应用。
Clin Neurophysiol. 2023 May;149:178-201. doi: 10.1016/j.clinph.2023.01.017. Epub 2023 Feb 13.
3
EEG is better left alone.
脑电图最好别去动它。
Sci Rep. 2023 Feb 9;13(1):2372. doi: 10.1038/s41598-023-27528-0.
4
Handling EEG artifacts and searching individually optimal experimental parameter in real time: a system development and demonstration.实时处理 EEG 伪迹并搜索个体最优实验参数：系统开发与演示。
J Neural Eng. 2022 Feb 2;19(1). doi: 10.1088/1741-2552/ac42b6.
5
Identifying key factors for improving ICA-based decomposition of EEG data in mobile and stationary experiments.识别提高基于 ICA 的 EEG 数据分解在移动和固定实验中性能的关键因素。
Eur J Neurosci. 2021 Dec;54(12):8406-8420. doi: 10.1111/ejn.14992. Epub 2020 Oct 15.
6
The Maryland analysis of developmental EEG (MADE) pipeline.马里兰州发育脑电图（MADE）分析管道。
Psychophysiology. 2020 Jun;57(6):e13580. doi: 10.1111/psyp.13580. Epub 2020 Apr 15.
7
Optimizing the ICA-based removal of ocular EEG artifacts from free viewing experiments.优化基于独立成分分析的自由观看实验中眼电伪迹的去除。
Neuroimage. 2020 Feb 15;207:116117. doi: 10.1016/j.neuroimage.2019.116117. Epub 2019 Nov 2.
8
Automagic: Standardized preprocessing of big EEG data.自动处理：大型 EEG 数据的标准化预处理。
Neuroimage. 2019 Oct 15;200:460-473. doi: 10.1016/j.neuroimage.2019.06.046. Epub 2019 Jun 21.
9
ICLabel: An automated electroencephalographic independent component classifier, dataset, and website.ICLabel：一种自动化的脑电图独立成分分类器、数据集和网站。
Neuroimage. 2019 Sep;198:181-197. doi: 10.1016/j.neuroimage.2019.05.026. Epub 2019 May 16.
10
Evaluation of Artifact Subspace Reconstruction for Automatic EEG Artifact Removal.用于自动去除脑电图伪迹的伪迹子空间重建评估
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1242-1245. doi: 10.1109/EMBC.2018.8512547.