自动化 EEG 大分析 II：事件相关特征的认知方面,NeuroImage

当前位置： X-MOL 学术 › NeuroImage › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

自动化 EEG 大分析 II：事件相关特征的认知方面
NeuroImage ( IF 4.7 ) Pub Date : 2020-02-01 , DOI: 10.1016/j.neuroimage.2019.116054
Nima Bigdely-Shamlo ₁ , Jonathan Touryan ₂ , Alejandro Ojeda ₃ , Christian Kothe ₃ , Tim Mullen ₃ , Kay Robbins ₄

Affiliation

我们根据在与四个不同机构相关的六个实验地点进行的 17 项研究的原始 EEG 数据，展示了对事件相关反应的大规模分析的结果。分析语料库代表 1,155 个记录，其中包含在几种不同的实验范式下获得的大约 780 万个事件实例。这种大规模分析基于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，将原始 EEG 转换为适合比较分析的形式。该分析的一个关键组成部分是使用通用词汇表来描述相关事件特征的研究特定事件代码的注释。我们证明了分层事件描述符（HED 标签）捕获了多个录音、主题、研究、范式、耳机配置和实验站点中常见的 EEG 事件的统计显着认知方面。我们使用表征相似性分析 (RSA) 来表明，使用相同认知方面注释的 EEG 响应比不共享该认知方面的 EEG 响应明显更相似。这些 RSA 相似性结果得到了利用这些关联的非线性相似性的可视化的支持。我们应用时间重叠回归，减少由相邻事件实例引起的混淆，提取时间和时频 EEG 特征（回归 ERP 和 ERSP），这些特征在研究之间具有可比性，并复制先前个别研究的结果。同样地，我们使用二级线性回归来分离所有研究中不同认知方面对这些特征的影响。这项工作表明，脑电图大型分析（跨研究汇集原始数据）可以比单一研究提供的更广泛的方式研究大脑动力学。一篇配套论文通过解决通道和偶极子水平研究中 EEG 的时间和频率统计特性的共性来补充这种基于事件的分析。

"点击查看英文标题和摘要"

Automated EEG mega-analysis II: Cognitive aspects of event related features

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.

更新日期：2020-02-01

点击分享查看原文

点击收藏

公开下载

阅读更多本刊新发论文本刊介绍/投稿指南