Cong Fengyu, Lin Qiu-Hua, Astikainen Piia, Ristaniemi Tapani
Department of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, China; Department of Mathematical Information Technology, University of Jyväskylä, Finland.
School of Information and Communication Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, China.
J Neurosci Methods. 2014 Oct 30;236:76-85. doi: 10.1016/j.jneumeth.2014.08.018. Epub 2014 Aug 23.
It 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.
When 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.
We 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.
The 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.
众所周知，事件相关电位（ERP）数据符合线性变换模型（LTM）。在使用主成分分析/独立成分分析（PCA/ICA）进行组水平ERP数据处理时，不同实验条件和不同参与者的ERP数据常常被拼接在一起。从理论上假设，不同的实验条件和不同的参与者具有相同的LTM。然而，如何根据信号处理方法来验证这一假设却鲜有报道。
当对一种刺激的ERP数据进行独立成分分析（ICA）全局优化时，我们得到了将大脑中的一个源映射到头皮上两点的两个系数之间的比率。基于这样的比率，我们定义了一个相对映射系数（RMC）。如果某一ERP在两种条件下的RMC在实际中无显著差异，那么该ERP在这两种条件下的映射系数在统计学上是相同的。
我们检验了ERP数据相同的LTM是否可应用于恐惧和快乐面部表情这两种不同的刺激类型。这些刺激用于成年人类参与者的忽略Oddball范式中。基于N170的RMC，我们发现N170对恐惧和快乐面孔反应的LTM（基于ICASSO）没有显著差异。
我们没有找到直接比较的方法。
基于ICA分解提出的RMC是验证实验条件之间ERP的LTM相似性的有效方法。这对于应用组水平的PCA/ICA处理ERP数据非常重要。
