The brain displays dynamical system behaviors at various levels that are functionally and cognitively relevant. Ample researches have examined how the dynamical properties of brain activity reflect the neural cognitive working mechanisms. A prevalent approach in this field is to extract the trial-averaged brain electrophysiological signals as a representation of the dynamical response of the complex neural system to external stimuli. However, the responses are intrinsically variable in latency from trial to trial. The variability compromises the accuracy of the detected dynamical response pattern based on trial-averaged approach, which may mislead subsequent modelling works. More accurate characterization of the brain’s dynamical response incorporating single trial variability information is of profound significance in deepening our understanding of neural cognitive dynamics and brain’s working principles. Various methods have been attempted to address the trial-to-trial asynchrony issue in order to achieve an improved representation of the dynamical response. We review the latest development of methodology in this area and the contribution of latency variability-based decomposition and reconstruction of dynamical response to neural cognitive researches.

大脑在功能和认知上相关的各个层面上显示动态系统行为。大量研究已经检验了大脑活动的动态特性如何反映神经认知工作机制。该领域的一种流行方法是提取试验平均脑电生理信号作为复杂神经系统对外部刺激的动态响应的表示。然而，每次试验的响应延迟本质上是可变的。这种变异性损害了基于试验平均方法检测到的动态响应模式的准确性，这可能会误导后续的建模工作。结合单次试验变异性信息更准确地表征大脑的动态反应对于加深我们对神经认知动力学和大脑工作原理的理解具有深远的意义。已经尝试了各种方法来解决试验间异步问题，以实现动态响应的改进表示。我们回顾了该领域方法论的最新发展，以及基于潜伏变异性的动态响应分解和重构对神经认知研究的贡献。