Automated EEG mega-analysis II: Cognitive aspects of event related features
Automated EEG mega-analysis II: Cognitive aspects of event related features
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DOI：
10.1016/j.neuroimage.2019.116054
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发表时间：
2020-02-15
期刊：
影响因子：
5.7
通讯作者：
Robbins, Kay
中科院分区：
文献类型：
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作者：
Bigdely-Shamlo, Nima;Touryan, Jonathan;Robbins, Kay
我们介绍了一项大规模分析事件相关反应的结果，该分析基于四个不同机构的六个试验点进行的17项研究的原始脑电数据。分析语料库代表1,155个记录，包含在几个不同实验范式下获得的大约780万个事件实例。这种大规模分析的前提是一致的数据组织和事件注释，以及有效的自动化预处理管道，以将原始脑电转换为适合比较分析的形式。这一分析的一个关键组成部分是使用通用词汇来描述相关事件特征的研究特定事件代码的注释。我们证明，分层事件描述符(HED标签)捕获了在多个记录、受试者、研究、范例、耳机配置和实验地点中常见的EEG事件的统计上重要的认知方面。我们使用表征相似性分析(RSA)来表明，用相同认知方面标注的EEG反应比那些不具有该认知方面的EEG反应明显更相似。利用这些关联的非线性相似性的可视化支持这些RSA相似性结果。我们应用时间重叠回归，减少相邻事件实例造成的混乱，以提取时间和时间-频率脑电特征(倒退的事件相关电位和事件相关电位)，这些特征在不同研究之间具有可比性，并复制先前个别研究的结果。同样，在所有研究中，我们使用二级线性回归来分离不同认知方面对这些特征的影响。这项工作表明，脑电巨型分析(汇集不同研究的原始数据)能够以比单一研究更普遍的方式研究大脑动力学。一篇配套的论文补充了这种基于事件的分析，解决了在通道和偶极水平上的研究中脑电的时间和频率统计特性的共性。
We present the results of a large-scale analysis of event-related responses based on raw EEG data from 17 studies performed at six experimental sites associated with four different institutions. The analysis corpus represents 1,155 recordings containing approximately 7.8 million event instances acquired under several different experimental paradigms. Such large-scale analysis is predicated on consistent data organization and event annotation as well as an effective automated preprocessing pipeline to transform raw EEG into a form suitable for comparative analysis. A key component of this analysis is the annotation of study-specific event codes using a common vocabulary to describe relevant event features. We demonstrate that Hierarchical Event Descriptors (HED tags) capture statistically significant cognitive aspects of EEG events common across multiple recordings, subjects, studies, paradigms, headset configurations, and experimental sites. We use representational similarity analysis (RSA) to show that EEG responses annotated with the same cognitive aspect are significantly more similar than those that do not share that cognitive aspect. These RSA similarity results are supported by visualizations that exploit the non-linear similarities of these associations. We apply temporal overlap regression, reducing confounds caused by adjacent event instances, to extract time and time-frequency EEG features (regressed ERPs and ERSPs) that are comparable across studies and replicate findings from prior, individual studies. Likewise, we use second-level linear regression to separate effects of different cognitive aspects on these features across all studies. This work demonstrates that EEG mega-analysis (pooling of raw data across studies) can enable investigations of brain dynamics in a more generalized fashion than single studies afford. A companion paper complements this event-based analysis by addressing commonality of the time and frequency statistical properties of EEG across studies at the channel and dipole level.