A Bayesian framework for unifying data cleaning, source separation and imaging of electroencephalographic signals
A Bayesian framework for unifying data cleaning, source separation and imaging of electroencephalographic signals
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DOI：
10.1101/559450
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发表时间：
2019-02
期刊：
影响因子：
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通讯作者：
A. Ojeda;Marius Klug;K. Kreutz-Delgado;K. Gramann;J. Mishra
中科院分区：
文献类型：
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作者：
A. Ojeda;Marius Klug;K. Kreutz-Delgado;K. Gramann;J. Mishra
脑电图（EEG）源成像依赖于复杂的信号处理算法来处理数据清洗、源分离和定位问题。通常，这些问题是由独立的启发式依次解决的，这限制了EEG图像在各种应用中的使用。在这里，我们提出了一个统一的经验贝叶斯框架，其中这些不同的问题可以用一个单一的算法来解决。我们使用空间稀疏性约束自适应地将脑源分离为具有已知解剖学支持的最大独立组件，同时最小化重叠的人工活动。该框架产生一个递归的逆时空过滤器，可用于离线和在线应用程序。我们称这种过滤器为递归稀疏贝叶斯学习（RSBL）。在理论方面，我们论证了Infomax独立分量分析和RSBL之间的联系。我们通过仿真表明，RSBL可以从噪声数据中分离和定位在空间和时间上重叠的皮质和伪成分。在实际数据中，我们使用RSBL分析单次试验误差相关电位，在扣带回中找到源。我们进一步在两个不相关的EEG研究上对我们的算法进行了基准测试，结果表明：1)在短时间尺度上，它在源分离方面优于Infomax； 2)与流行的伪影子空间去除算法不同，它可以在不显著扭曲干净时代的情况下减少伪影。最后，我们分析了移动脑/身体成像数据，以表征在全身旋转过程中支持头部计算的脑动力学，复制了先前实验文献的主要发现。
Electroencephalographic (EEG) source imaging depends upon sophisticated signal processing algorithms to deal with the problems of data cleaning, source separation, and localization. Typically, these problems are sequentially addressed by independent heuristics, limiting the use of EEG images on a variety of applications. Here, we propose a unifying empirical Bayes framework in which these dissimilar problems can be solved using a single algorithm. We use spatial sparsity constraints to adaptively segregate brain sources into maximally independent components with known anatomical support, while minimally overlapping artifactual activity. The framework yields a recursive inverse spatiotemporal filter that can be used for offline and online applications. We call this filter Recursive Sparse Bayesian Learning (RSBL). Of theoretical relevance, we demonstrate the connections between Infomax Independent Component Analysis and RSBL. We use simulations to show that RSBL can separate and localize cortical and artifact components that overlap in space and time from noisy data. On real data, we use RSBL to analyze single-trial error-related potentials, finding sources in the cingulate gyrus. We further benchmark our algorithm on two unrelated EEG studies showing that: 1) it outperforms Infomax for source separation on short time-scales and 2), unlike the popular Artifact Subspace Removal algorithm, it can reduce artifacts without significantly distorting clean epochs. Finally, we analyze mobile brain/body imaging data to characterize the brain dynamics supporting heading computation during full-body rotations, replicating the main findings of previous experimental literature.