Estimating neural activity from visual areas using functionally defined EEG templates.
Estimating neural activity from visual areas using functionally defined EEG templates.
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DOI：
10.1002/hbm.26188
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发表时间：
2023-04-01
影响因子：
4.8
通讯作者：
中科院分区：
文献类型：
--
作者：
脑电图（EEG）是记录人类神经活动的常见且廉价的方法。然而，它缺乏空间分辨率，使得难以确定大脑的哪些区域负责观察到的EEG响应。在这里，我们提出了一种新的易于使用的方法，依赖于脑电图地形模板。使用50名参与者的MRI和fMRI扫描，我们模拟了每个视觉区域的活动如何出现在头皮上，并对该信号进行平均，以产生功能定义的EEG模板。一旦创建，这些模板可以用于估计每个视觉区域对所观察到的EEG活动的贡献。我们在广泛的模拟和真实的数据上测试了这种方法。所提出的程序是定制的个人源定位方法一样好，鲁棒性广泛的因素，并具有几个优势。首先，因为它不依赖于个人的大脑扫描，它是廉价的，可以用于任何脑电图数据集，过去或现在。其次，这些结果很容易用功能性大脑区域来解释，并且可以在神经成像技术中进行比较。最后，该方法易于理解，使用简单，可扩展到其他脑源。我们提出了一种新的方法，定位和估计脑源的脑电图（EEG）信号的贡献。该方法基于EEG模板恢复功能源，允许在神经成像研究中进行比较。它是强大的，快速的，易于使用的，廉价的，并可以应用于现有的数据集。
Electroencephalography (EEG) is a common and inexpensive method to record neural activity in humans. However, it lacks spatial resolution making it difficult to determine which areas of the brain are responsible for the observed EEG response. Here we present a new easy‐to‐use method that relies on EEG topographical templates. Using MRI and fMRI scans of 50 participants, we simulated how the activity in each visual area appears on the scalp and averaged this signal to produce functionally defined EEG templates. Once created, these templates can be used to estimate how much each visual area contributes to the observed EEG activity. We tested this method on extensive simulations and on real data. The proposed procedure is as good as bespoke individual source localization methods, robust to a wide range of factors, and has several strengths. First, because it does not rely on individual brain scans, it is inexpensive and can be used on any EEG data set, past or present. Second, the results are readily interpretable in terms of functional brain regions and can be compared across neuroimaging techniques. Finally, this method is easy to understand, simple to use and expandable to other brain sources. We present a new method that locates and estimates the contribution of brain sources to an electroencephalography (EEG) signal. This method recovers functional sources based on EEG templates, allowing comparisons across neuroimaging studies. It is robust, quick, easy to use, inexpensive, and can be applied to existing data sets.
影响因子：
5.7
作者：
Cottereau BR;Ales JM;Norcia AM
通讯作者：
Norcia AM
影响因子：
4.8
作者：
Cottereau, Benoit R.;Ales, Justin M.;Norcia, Anthony M.
通讯作者：
Norcia, Anthony M.
影响因子：
4.3
作者：
Rina A;Papanikolaou A;Zong X;Papageorgiou DT;Keliris GA;Smirnakis SM
通讯作者：
Smirnakis SM