工件(错误)
脑电图
计算机科学
管道(软件)
人工智能
小波
模式识别(心理学)
灵敏度(控制系统)
一致性(知识库)
工程类
心理学
电子工程
精神科
程序设计语言
作者
Neil W. Bailey,Mana Biabani,Aron T. Hill,Aleksandra Miljevic,Nigel C. Rogasch,Brittany McQueen,Oscar W. Murphy,Paul B. Fitzgerald
标识
DOI:10.1016/j.clinph.2023.01.017
摘要
Electroencephalographic (EEG) data are often contaminated with non-neural artifacts which can confound experimental results. Current artifact cleaning approaches often require costly manual input. Our aim was to provide a fully automated EEG cleaning pipeline that addresses all artifact types and improves measurement of EEG outcomes METHODS: We developed RELAX (the Reduction of Electroencephalographic Artifacts). RELAX cleans continuous data using Multi-channel Wiener filtering [MWF] and/or wavelet enhanced independent component analysis [wICA] applied to artifacts identified by ICLabel [wICA_ICLabel]). Several versions of RELAX were compared using three datasets (N = 213, 60 and 23 respectively) against six commonly used pipelines across a range of artifact cleaning metrics, including measures of remaining blink and muscle activity, and the variance explained by experimental manipulations after cleaning.RELAX with MWF and wICA_ICLabel showed amongst the best performance at cleaning blink and muscle artifacts while preserving neural signal. RELAX with wICA_ICLabel only may perform better at differentiating alpha oscillations between working memory conditions.RELAX provides automated, objective and high-performing EEG cleaning, is easy to use, and freely available on GitHub.We recommend RELAX for data cleaning across EEG studies to reduce artifact confounds, improve outcome measurement and improve inter-study consistency.
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