How to validate similarity in linear transform models of event-related potentials between experimental conditions?
How to validate similarity in linear transform models of event-related potentials between experimental conditions?
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DOI：
10.1016/j.jneumeth.2014.08.018
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发表时间：
2014-10
影响因子：
3
通讯作者：
F. Cong;Qiuhua Lin;P. Astikainen;T. Ristaniemi
中科院分区：
文献类型：
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作者：
F. Cong;Qiuhua Lin;P. Astikainen;T. Ristaniemi
背景众所周知，事件相关电位（ERPs）数据符合线性变换模型（LTM）。在使用主成分/独立成分分析（PCA/伊卡）进行群体水平的ERP数据处理时，常常需要将不同实验条件和不同被试的ERP数据连接起来。理论上假定不同的实验条件和不同的参与者具有相同的LTM。新方法当伊卡分解对一个刺激的ERP数据进行全局优化时，我们得到了将大脑中的一个源映射到头皮沿着的两个点的两个系数之间的比值。在此基础上，定义了相对映射系数（RMC）。如果RMCs之间的两个条件的ERP是没有显着不同的实践中，这两个条件之间的ERP映射系数是统计学上identic.ResultsWe研究是否相同的LTM的ERP数据可以应用于两种不同的刺激类型的恐惧和快乐的面部表情。他们在成年人参与者中被用于忽略古怪的范例。我们没有发现显着差异的LTMS（基于ICASSO）的N170反应的恐惧和快乐的面孔在RMC的N170。与现有的方法比较（S），我们发现没有直接comparing.ConclusionsThe建议的RMC在伊卡分解光是一个有效的方法，验证实验条件之间的ERPs的LTMS的相似性。这对于应用组级PCA/伊卡处理ERP数据是非常基础的。
BackgroundIt is well-known that data of event-related potentials (ERPs) conform to the linear transform model (LTM). For group-level ERP data processing using principal/independent component analysis (PCA/ICA), ERP data of different experimental conditions and different participants are often concatenated. It is theoretically assumed that different experimental conditions and different participants possess the same LTM. However, how to validate the assumption has been seldom reported in terms of signal processing methods.New methodWhen ICA decomposition is globally optimized for ERP data of one stimulus, we gain the ratio between two coefficients mapping a source in brain to two points along the scalp. Based on such a ratio, we defined a relative mapping coefficient (RMC). If RMCs between two conditions for an ERP are not significantly different in practice, mapping coefficients of this ERP between the two conditions are statistically identical.ResultsWe examined whether the same LTM of ERP data could be applied for two different stimulus types of fearful and happy facial expressions. They were used in an ignore oddball paradigm in adult human participants. We found no significant difference in LTMs (based on ICASSO) of N170 responses to the fearful and the happy faces in terms of RMCs of N170.Comparison with existing method(s)We found no methods for straightforward comparison.ConclusionsThe proposed RMC in light of ICA decomposition is an effective approach for validating the similarity of LTMs of ERPs between experimental conditions. This is very fundamental to apply group-level PCA/ICA to process ERP data.