EEG Data Quality in Real-World Settings: Examining Neural Correlates of Attention in School-Aged Children.
EEG Data Quality in Real-World Settings: Examining Neural Correlates of Attention in School-Aged Children.
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现实世界中的脑电图数据质量：检查学龄儿童注意力的神经相关性。
DOI：
10.1111/mbe.12314
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Grammer,JennieK
中科院分区：
文献类型：
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作者：
Xu,Keye;Torgrimson,SarahJo;Torres,Remi;Lenartowicz,Agatha;Grammer,JennieK
移动的脑电图（EEG）技术的进步使得在真实的世界环境中检查隐蔽认知过程成为可能，例如学生在课堂上的注意力。在这里，我们概述了使用有线和无线EEG技术的研究，以检查小学儿童在学校中越来越自然的范式中的注意力，从基于实验室的范式，儿童在现场实验室与实验者一对一见面，到在同一所学校的半自然课堂课程中进行的移动的EEG测试。尽管以课堂为基础的范式增加了数据丢失，但我们证明了在幼儿的课堂环境中收集高质量数据是可行的。我们还提供了一个测试案例，说明如何使用强大的EEG信号（如α振荡）来识别隐蔽过程（如教室中的注意力）中的可测量差异。最后，我们为有兴趣在真实的世界中采用自然EEG方法的研究人员提供了实用的建议。
Advances in mobile electroencephalography (EEG) technology have made it possible to examine covert cognitive processes in real‐world settings such as student attention in the classroom. Here, we outline research using wired and wireless EEG technology to examine attention in elementary school children across increasingly naturalistic paradigms in schools, ranging from a lab‐based paradigm where children met one‐on‐one with an experimenter in a field laboratory to mobile EEG testing conducted in the same school during semi‐naturalistic classroom lessons. Despite an increase of data loss with the classroom‐based paradigm, we demonstrate that it is feasible to collect quality data in classroom settings with young children. We also provide a test case for how robust EEG signals, such as alpha oscillations, can be used to identify measurable differences in covert processes like attention in classrooms. We end with pragmatic suggestions for researchers interested in employing naturalistic EEG methods in real‐world, multisensory contexts.