论文标题
刺激性的刺激性大脑反应的广义典型相关性分析
Stimulus-Informed Generalized Canonical Correlation Analysis of Stimulus-Following Brain Responses
论文作者
论文摘要
在脑部计算机界面或神经科学应用中，普遍的规范相关分析（GCCA）通常用于在不同受试者的神经活动中提取相关的信号成分，这些受试者的神经活动是相同的刺激。这允许量化所谓的受试者间相关性或相对于其他（非）神经活动的刺激性脑响应的信噪比。但是，GCCA是刺激性的：它不考虑刺激信息，因此不能很好地应对较低量的数据或较小的受试者组。我们提出了一种基于Maxvar-GCCA框架的新型刺激性GCCA算法。我们基于一组听取相同语音刺激的受试者的脑电图响应之间的受试者间相关性，展示了提出的刺激信息GCCA方法的优越性，尤其是对于较低量的数据或较小的受试者。
In brain-computer interface or neuroscience applications, generalized canonical correlation analysis (GCCA) is often used to extract correlated signal components in the neural activity of different subjects attending to the same stimulus. This allows quantifying the so-called inter-subject correlation or boosting the signal-to-noise ratio of the stimulus-following brain responses with respect to other (non-)neural activity. GCCA is, however, stimulus-unaware: it does not take the stimulus information into account and does therefore not cope well with lower amounts of data or smaller groups of subjects. We propose a novel stimulus-informed GCCA algorithm based on the MAXVAR-GCCA framework. We show the superiority of the proposed stimulus-informed GCCA method based on the inter-subject correlation between electroencephalography responses of a group of subjects listening to the same speech stimulus, especially for lower amounts of data or smaller groups of subjects.
