Ouyang Guang, Sommer Werner, Zhou Changsong
Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong; Centre for Nonlinear Studies and The Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong.
Department of Psychology, Humboldt-Universität zu Berlin, D-10099 Berlin, Germany.
J Neurosci Methods. 2015 Jul 30;250:7-21. doi: 10.1016/j.jneumeth.2014.10.009. Epub 2014 Oct 23.
Conventionally, event-related brain potentials (ERPs) are obtained by averaging a number of single trials. This can be problematic due to trial-to-trial latency variability. Residue iteration decomposition (RIDE) was developed to decompose ERPs into component clusters with different latency variability and to re-synchronize the separated components into a reconstructed ERP.
RIDE has been continuously upgraded and now converges to a robust version. We describe the principles of RIDE and detailed algorithms of the functional modules of a toolbox. We give recommendations and provide caveats for using RIDE from both methodological and psychological perspectives.
RIDE was applied to several data samples to demonstrate its ability to decompose and reconstruct latency-variable components of ERPs and to retrieve single trial variability information. Different functionalities of RIDE were shown in appropriate examples.
RIDE employs several modules to achieve a robust decomposition of ERP. As main innovations RIDE (1) is able to extract components based on the combination of known event markers and estimated latencies, (2) prevents distortions much more effectively than previous methods based on least-square algorithms, and (3) allows time window confinements to target relevant components associated with sub-processes of interest.
RIDE is a convenient method that decomposes ERPs and provides single trial analysis, yielding rich information about sub-components, and that reconstructs ERPs, more closely reflecting the combined activity of single trial ERPs. The outcomes of RIDE provide new dimensions to study brain-behavior relationships based on EEG data.
传统上，事件相关脑电位（ERP）是通过对多个单次试验进行平均获得的。由于试验间潜伏期的变异性，这可能会产生问题。残差迭代分解（RIDE）技术被开发出来，用于将ERP分解为具有不同潜伏期变异性的成分簇，并将分离出的成分重新同步为一个重建的ERP。
RIDE不断升级，现在已发展为一个稳健的版本。我们描述了RIDE的原理以及一个工具箱功能模块的详细算法。我们从方法学和心理学角度给出使用RIDE的建议并提供注意事项。
RIDE被应用于几个数据样本，以证明其分解和重建ERP潜伏期可变成分以及获取单次试验变异性信息的能力。在适当的例子中展示了RIDE的不同功能。
RIDE采用多个模块来实现对ERP的稳健分解。作为主要创新点，RIDE（1）能够基于已知事件标记和估计潜伏期的组合来提取成分，（2）比基于最小二乘算法的先前方法更有效地防止失真，（3）允许对时间窗口进行限制，以针对与感兴趣的子过程相关的目标成分。
RIDE是一种方便的方法，它可以分解ERP并提供单次试验分析，产生关于子成分的丰富信息，并且能够重建ERP，更紧密地反映单次试验ERP的综合活动。RIDE的结果为基于脑电图数据研究脑-行为关系提供了新的维度。
