Unfold: an integrated toolbox for overlap correction, non-linear modeling, and regression-based EEG analysis
Unfold: an integrated toolbox for overlap correction, non-linear modeling, and regression-based EEG analysis
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DOI：
10.7717/peerj.7838
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发表时间：
2019-10-24
期刊：
影响因子：
2.7
通讯作者：
Dimigen, Olaf
中科院分区：
文献类型：
--
作者：
Ehinger, Benedikt, V;Dimigen, Olaf
基于事件相关脑电位(ERPs)的电生理研究正日益从简单、严格的正交刺激范式向更复杂、准实验设计和涉及快速、多感觉刺激和复杂运动行为的自然情境发展。因此，来自后续事件的电生理反应通常彼此重叠。此外，记录的神经活动通常由许多协变量调制，这些协变量以线性或非线性的方式影响测量的响应。无法避免系统性时间重叠变化和条件之间低水平混淆的范例的例子包括自然视觉期间的脑电(EEG)/眼睛跟踪联合实验、快速多感觉刺激实验和移动脑/身体成像研究。然而，即使是“传统的”、高度受控的ERP数据集通常也包含重叠活动的隐藏混合(例如，来自刺激开始、非自愿的微扫视或按钮按下)，将这些组成部分分离出来对于正确解释结果是有帮助的，甚至是必要的。在本文中，我们介绍了一个强大的，但易于使用的基于回归的脑电信号分析的MATLAB工具箱，它将现有的大规模单变量建模(“回归-ERPs”)、线性反卷积建模和非线性建模的概念与广义相加模型结合到一个连贯而灵活的分析框架中。该工具箱是模块化的，与EEGLAB兼容，甚至可以高效地处理大型数据集。它还包括用于正则化和使用时间基函数(例如，傅立叶集)的高级选项。我们用模拟数据和标准人脸识别实验的数据说明了这种方法的优势。除了传统和非传统的脑电/事件相关电位设计，展开还可以应用于其他重叠的生理信号，如瞳孔或电皮肤反应。
Electrophysiological research with event-related brain potentials (ERPs) is increasingly moving from simple, strictly orthogonal stimulation paradigms towards more complex, quasi-experimental designs and naturalistic situations that involve fast, multisensory stimulation and complex motor behavior. As a result, electrophysiological responses from subsequent events often overlap with each other. In addition, the recorded neural activity is typically modulated by numerous covariates, which influence the measured responses in a linear or non-linear fashion. Examples of paradigms where systematic temporal overlap variations and low-level confounds between conditions cannot be avoided include combined electroencephalogram (EEG)/eye-tracking experiments during natural vision, fast multisensory stimulation experiments, and mobile brain/body imaging studies. However, even "traditional," highly controlled ERP datasets often contain a hidden mix of overlapping activity (e.g., from stimulus onsets, involuntary microsaccades, or button presses) and it is helpful or even necessary to disentangle these components for a correct interpretation of the results. In this paper, we introduce unfold, a powerful, yet easy-to-use MATLAB toolbox for regression-based EEG analyses that combines existing concepts of massive univariate modeling ("regression-ERPs"), linear deconvolution modeling, and non-linear modeling with the generalized additive model into one coherent and flexible analysis framework. The toolbox is modular, compatible with EEGLAB and can handle even large datasets efficiently. It also includes advanced options for regularization and the use of temporal basis functions (e.g., Fourier sets). We illustrate the advantages of this approach for simulated data as well as data from a standard face recognition experiment. In addition to traditional and non-conventional EEG/ERP designs, unfold can also be applied to other overlapping physiological signals, such as pupillary or electrodermal responses.