{"title":"结合独立成分分析和主成分分析的半自动脑电信号预处理方案。","authors":"Guang Ouyang, Yingzhe Li","doi":"10.1016/j.xpro.2025.103682","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":34214,"journal":{"name":"STAR Protocols","volume":"6 1","pages":"103682"},"PeriodicalIF":1.3000,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11930125/pdf/","citationCount":"0","resultStr":"{\"title\":\"Protocol for semi-automatic EEG preprocessing incorporating independent component analysis and principal component analysis.\",\"authors\":\"Guang Ouyang, Yingzhe Li\",\"doi\":\"10.1016/j.xpro.2025.103682\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":34214,\"journal\":{\"name\":\"STAR Protocols\",\"volume\":\"6 1\",\"pages\":\"103682\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2025-03-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11930125/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"STAR Protocols\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.xpro.2025.103682\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/3/6 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q4\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"STAR Protocols","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.xpro.2025.103682","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/3/6 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Protocol for semi-automatic EEG preprocessing incorporating independent component analysis and principal component analysis.
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.
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