Bayesian models for event-related potentials time-series and electrode correlations estimation
Bayesian models for event-related potentials time-series and electrode correlations estimation
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DOI：
10.1101/2022.08.02.502520
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Simon Busch-Moreno;Xiao Fu;E. Roesch
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文献类型：
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作者：
Simon Busch-Moreno;Xiao Fu;E. Roesch
贝叶斯推断和预测对事件相关电位（ERP）影响的最新发展源于各种各样的方法，包括机器学习，多级模型等。然而，这些方法中很少有利用跨时间和跨空间（头皮）的ERP电压的明确估计。在本文中，通过迭代过程，我们提出了高斯随机游走（GRW）模型，可以估计电压随时间的不确定性，并提供跨电极的相关矩阵。我们将这些模型应用到真实的ERP数据作为一个例子，从P3B范式。我们讨论的结果，在过去和目前的文献中的ERP估计和脑电图分析一般。
Recent developments on Bayesian inference and prediction for effects on event-related potentials (ERPs) stem from a wide variety of methods, including machine learning, multilevel models, and others. However, few of these approaches make use of clear estimates of the ERP voltage across time and across space (scalp). In the present article, via an iterative process, we propose Gaussian random walk (GRW) models that can estimate voltage uncertainty across time and also provide correlation matrices across electrodes. We apply these models to real ERP data from a P3b paradigm as an example. We discuss results in terms of past and current literature of both ERP estimation and electroencephalography analysis in general.