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黄昏到黎明:一个用于使用伪迹子空间重构自动清理整夜睡眠脑电图的EEGLAB插件。

Dusk2Dawn: an EEGLAB plugin for automatic cleaning of whole-night sleep electroencephalogram using Artifact Subspace Reconstruction.

作者信息

Somervail Richard, Cataldi Jacinthe, Stephan Aurélie M, Siclari Francesca, Iannetti Gian Domenico

机构信息

Neuroscience and Behaviour Laboratory, Italian Institute of Technology (IIT), Rome, Italy.

Department of Neuroscience Physiology and Pharmacology, University College London (UCL), London, UK.

出版信息

Sleep. 2023 Dec 11;46(12). doi: 10.1093/sleep/zsad208.

DOI:10.1093/sleep/zsad208
PMID:37542730
Abstract

Whole-night sleep electroencephalogram (EEG) is plagued by several types of large-amplitude artifacts. Common approaches to remove them are fraught with issues: channel interpolation, rejection of noisy intervals, and independent component analysis are time-consuming, rely on subjective user decisions, and result in signal loss. Artifact Subspace Reconstruction (ASR) is an increasingly popular approach to rapidly and automatically clean wake EEG data. Indeed, ASR adaptively removes large-amplitude artifacts regardless of their scalp topography or consistency throughout the recording. This makes ASR, at least in theory, a highly-promising tool to clean whole-night EEG. However, ASR crucially relies on calibration against a subset of relatively clean "baseline" data. This is problematic when the baseline changes substantially over time, as in whole-night EEG data. Here we tackled this issue and, for the first time, validated ASR for cleaning sleep EEG. We demonstrate that ASR applied out-of-the-box, with the parameters recommended for wake EEG, results in the dramatic removal of slow waves. We also provide an appropriate procedure to use ASR for automatic and rapid cleaning of whole-night sleep EEG data or any long EEG recording. Our procedure is freely available in Dusk2Dawn, an open-source plugin for EEGLAB.

摘要

整夜睡眠脑电图(EEG)受到几种大振幅伪迹的困扰。去除这些伪迹的常用方法存在诸多问题:通道插值、去除噪声区间以及独立成分分析都很耗时,依赖用户主观判断,并且会导致信号丢失。伪迹子空间重建(ASR)是一种越来越流行的用于快速自动清理清醒EEG数据的方法。实际上,ASR能自适应地去除大振幅伪迹,而不管其头皮地形图或整个记录过程中的一致性如何。这使得ASR至少在理论上成为清理整夜EEG的极具前景的工具。然而,ASR关键依赖于针对相对干净的“基线”数据子集进行校准。当基线随时间大幅变化时,如在整夜EEG数据中,这就成了问题。在此,我们解决了这个问题,并首次验证了ASR用于清理睡眠EEG的有效性。我们证明,按照为清醒EEG推荐的参数直接应用ASR,会导致慢波被大幅去除。我们还提供了一种适当的程序,用于使用ASR自动快速清理整夜睡眠EEG数据或任何长时间的EEG记录。我们的程序可在Dusk2Dawn中免费获取,Dusk2Dawn是EEGLAB的一个开源插件。

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