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首页> 外文期刊>BMC Neuroscience >FACET – a “Flexible Artifact Correction and Evaluation Toolbox” for concurrently recorded EEG/fMRI data
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FACET – a “Flexible Artifact Correction and Evaluation Toolbox” for concurrently recorded EEG/fMRI data

机译：FACET –“灵活的伪影校正和评估工具箱”，用于同时记录EEG / fMRI数据

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Background In concurrent EEG/fMRI recordings, EEG data are impaired by the fMRI gradient artifacts which exceed the EEG signal by several orders of magnitude. While several algorithms exist to correct the EEG data, these algorithms lack the flexibility to either leave out or add new steps. The here presented open-source MATLAB toolbox FACET is a modular toolbox for the fast and flexible correction and evaluation of imaging artifacts from concurrently recorded EEG datasets. It consists of an Analysis , a Correction and an Evaluation framework allowing the user to choose from different artifact correction methods with various pre- and post-processing steps to form flexible combinations. The quality of the chosen correction approach can then be evaluated and compared to different settings. Results FACET was evaluated on a dataset provided with the FMRIB plugin for EEGLAB using two different correction approaches: Averaged Artifact Subtraction (AAS, Allen et al., NeuroImage 12(2):230–239, 2000) and the FMRI Artifact Slice Template Removal (FASTR, Niazy et al., NeuroImage 28(3):720–737, 2005). Evaluation of the obtained results were compared to the FASTR algorithm implemented in the EEGLAB plugin FMRIB. No differences were found between the FACET implementation of FASTR and the original algorithm across all gradient artifact relevant performance indices. Conclusion The FACET toolbox not only provides facilities for all three modalities: data analysis, artifact correction as well as evaluation and documentation of the results but it also offers an easily extendable framework for development and evaluation of new approaches.
机译：背景技术在同时进行的EEG / fMRI记录中，fMRI梯度伪影会损害EEG数据，这些伪影会超过EEG信号几个数量级。尽管存在几种校正EEG数据的算法，但这些算法缺乏灵活性，可以省去或增加新的步骤。这里介绍的开源MATLAB工具箱FACET是一个模块化工具箱，用于快速，灵活地校正和评估来自同时记录的EEG数据集的成像伪像。它由一个Analysis，Correction和Evaluation框架组成，允许用户从具有各种预处理和后处理步骤的灵活的矫正方法中进行选择，以形成灵活的组合。然后可以评估所选校正方法的质量，并将其与不同的设置进行比较。结果使用两种不同的校正方法在FMRIB插件随附的EEGLAB数据集上评估了FACET：平均伪影减法（AAS，Allen等人，NeuroImage 12（2）：230–239，2000）和FMRI伪影切片模板去除（FASTR，Niazy等人，NeuroImage 28（3）：720-737，2005年）。将获得的结果的评估与EEGLAB插件FMRIB中实现的FASTR算法进行比较。在所有梯度伪影相关性能指标上，FASTR的FACET实现与原始算法之间没有发现差异。结束语FACET工具箱不仅为所有三种模式提供了便利：数据分析，工件校正以及结果的评估和记录，而且还为开发和评估新方法提供了易于扩展的框架。

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