Cox Roy, Weber Frederik D, Van Someren Eus J W
Department of Sleep and Cognition, Netherlands Institute for Neuroscience, Amsterdam, Netherlands.
Donders Institute for Brain, Cognition and Behavior, Nijmegen, Netherlands.
Front Neuroinform. 2024 Aug 9;18:1415512. doi: 10.3389/fninf.2024.1415512. eCollection 2024.
While standard polysomnography has revealed the importance of the sleeping brain in health and disease, more specific insight into the relevant brain circuits requires high-density electroencephalography (EEG). However, identifying and handling sleep EEG artifacts becomes increasingly challenging with higher channel counts and/or volume of recordings. Whereas manual cleaning is time-consuming, subjective, and often yields data loss (e.g., complete removal of channels or epochs), automated approaches suitable and practical for overnight sleep EEG remain limited, especially when control over detection and repair behavior is desired. Here, we introduce a flexible approach for automated cleaning of multichannel sleep recordings, as part of the free Matlab-based toolbox SleepTrip. Key functionality includes 1) channel-wise detection of various artifact types encountered in sleep EEG, 2) channel- and time-resolved marking of data segments for repair through interpolation, and 3) visualization options to review and monitor performance. Functionality for Independent Component Analysis is also included. Extensive customization options allow tailoring cleaning behavior to data properties and analysis goals. By enabling computationally efficient and flexible automated data cleaning, this tool helps to facilitate fundamental and clinical sleep EEG research.
虽然标准多导睡眠图已揭示睡眠大脑在健康和疾病中的重要性,但要更深入了解相关脑回路则需要高密度脑电图(EEG)。然而,随着通道数量增加和/或记录量增大,识别和处理睡眠EEG伪迹变得越来越具有挑战性。手动清理既耗时又主观,还常常导致数据丢失(例如,完全移除通道或时段),适用于整夜睡眠EEG且实用的自动化方法仍然有限,尤其是当需要对检测和修复行为进行控制时。在此,我们介绍一种灵活的方法,用于自动清理多通道睡眠记录,这是基于Matlab的免费工具箱SleepTrip的一部分。关键功能包括:1)按通道检测睡眠EEG中遇到的各种伪迹类型;2)通过插值对数据段进行通道和时间分辨标记以进行修复;3)用于查看和监测性能的可视化选项。还包括独立成分分析功能。广泛的定制选项允许根据数据特性和分析目标调整清理行为。通过实现高效且灵活的自动数据清理,该工具有助于促进基础和临床睡眠EEG研究。